{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       " 'piecewise',\n",
       " 'place',\n",
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       " 'polyadd',\n",
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       " 'polydiv',\n",
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       " 'polynomial',\n",
       " 'polysub',\n",
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       " 'product',\n",
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       " 'ptp',\n",
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       " 'putmask',\n",
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       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
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       " 'sum',\n",
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       " 'take',\n",
       " 'take_along_axis',\n",
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       " 'tanh',\n",
       " 'tensordot',\n",
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       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: pandas in e:\\w\\lib\\site-packages (from tushare) (2.2.2)\n",
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      "Collecting bs4 (from tushare)\n",
      "  Downloading bs4-0.0.2-py2.py3-none-any.whl.metadata (411 bytes)\n",
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      "   -------------------------------------- 142.3/142.3 kB 602.8 kB/s eta 0:00:00\n",
      "Downloading bs4-0.0.2-py2.py3-none-any.whl (1.2 kB)\n",
      "Downloading simplejson-3.19.3-cp312-cp312-win_amd64.whl (75 kB)\n",
      "   ---------------------------------------- 0.0/75.6 kB ? eta -:--:--\n",
      "   --------------------- ------------------ 41.0/75.6 kB 960.0 kB/s eta 0:00:01\n",
      "   ---------------------------------------- 75.6/75.6 kB 1.0 MB/s eta 0:00:00\n",
      "Installing collected packages: simplejson, bs4, tushare\n",
      "Successfully installed bs4-0.0.2 simplejson-3.19.3 tushare-1.4.14\n"
     ]
    }
   ],
   "source": [
    "pip install tushare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000004.SZ   20241225  14.63  14.63  14.63  14.63      16.25   -1.62   \n",
      "1     000004.SZ   20241224  16.25  16.58  16.25  16.25      18.05   -1.80   \n",
      "2     000004.SZ   20241223  18.11  19.41  18.05  18.05      20.06   -2.01   \n",
      "3     000004.SZ   20241220  19.62  20.59  19.28  20.06      20.63   -0.57   \n",
      "4     000004.SZ   20241219  18.75  20.63  18.74  20.63      18.75    1.88   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3384  000004.SZ   20100224   9.10   9.45   9.02   9.40       9.10    0.30   \n",
      "3385  000004.SZ   20100223   9.10   9.22   8.88   9.10       9.22   -0.12   \n",
      "3386  000004.SZ   20100222   9.09   9.30   8.82   9.22       9.17    0.05   \n",
      "3387  000004.SZ   20100212   9.10   9.34   9.03   9.17       9.50   -0.33   \n",
      "3388  000004.SZ   20100211   9.50   9.94   9.50   9.50      10.00   -0.50   \n",
      "\n",
      "      pct_chg        vol       amount  \n",
      "0     -9.9692   35453.00   51867.7390  \n",
      "1     -9.9723   63387.00  103125.2060  \n",
      "2    -10.0199  252984.24  466472.4850  \n",
      "3     -2.7630  319186.05  638201.8500  \n",
      "4     10.0267  366653.18  751937.6560  \n",
      "...       ...        ...          ...  \n",
      "3384   3.3000   13111.33   12122.3881  \n",
      "3385  -1.3000   17904.55   16210.6835  \n",
      "3386   0.5500   24813.10   22488.1075  \n",
      "3387  -3.4700   32126.76   29186.3041  \n",
      "3388  -5.0000   37140.43   35341.7808  \n",
      "\n",
      "[3389 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('117f2a8a0ae8eebfad203495e432895aaac096a1ac7598f7402a9756')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000004.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000004.SZ   20241225  14.63  14.63  14.63  14.63      16.25   -1.62   \n",
      "1     000004.SZ   20241224  16.25  16.58  16.25  16.25      18.05   -1.80   \n",
      "2     000004.SZ   20241223  18.11  19.41  18.05  18.05      20.06   -2.01   \n",
      "3     000004.SZ   20241220  19.62  20.59  19.28  20.06      20.63   -0.57   \n",
      "4     000004.SZ   20241219  18.75  20.63  18.74  20.63      18.75    1.88   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3384  000004.SZ   20100224   9.10   9.45   9.02   9.40       9.10    0.30   \n",
      "3385  000004.SZ   20100223   9.10   9.22   8.88   9.10       9.22   -0.12   \n",
      "3386  000004.SZ   20100222   9.09   9.30   8.82   9.22       9.17    0.05   \n",
      "3387  000004.SZ   20100212   9.10   9.34   9.03   9.17       9.50   -0.33   \n",
      "3388  000004.SZ   20100211   9.50   9.94   9.50   9.50      10.00   -0.50   \n",
      "\n",
      "      pct_chg        vol       amount  \n",
      "0     -9.9692   35453.00   51867.7390  \n",
      "1     -9.9723   63387.00  103125.2060  \n",
      "2    -10.0199  252984.24  466472.4850  \n",
      "3     -2.7630  319186.05  638201.8500  \n",
      "4     10.0267  366653.18  751937.6560  \n",
      "...       ...        ...          ...  \n",
      "3384   3.3000   13111.33   12122.3881  \n",
      "3385  -1.3000   17904.55   16210.6835  \n",
      "3386   0.5500   24813.10   22488.1075  \n",
      "3387  -3.4700   32126.76   29186.3041  \n",
      "3388  -5.0000   37140.43   35341.7808  \n",
      "\n",
      "[3389 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"stock000004.csv\")\n",
    "stockname='国华网安'\n",
    "stockcode='000004'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3379</th>\n",
       "      <th>3380</th>\n",
       "      <th>3381</th>\n",
       "      <th>3382</th>\n",
       "      <th>3383</th>\n",
       "      <th>3384</th>\n",
       "      <th>3385</th>\n",
       "      <th>3386</th>\n",
       "      <th>3387</th>\n",
       "      <th>3388</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "      <td>000004.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100303</td>\n",
       "      <td>20100302</td>\n",
       "      <td>20100301</td>\n",
       "      <td>20100226</td>\n",
       "      <td>20100225</td>\n",
       "      <td>20100224</td>\n",
       "      <td>20100223</td>\n",
       "      <td>20100222</td>\n",
       "      <td>20100212</td>\n",
       "      <td>20100211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>14.63</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.11</td>\n",
       "      <td>19.62</td>\n",
       "      <td>18.75</td>\n",
       "      <td>18.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.88</td>\n",
       "      <td>20.03</td>\n",
       "      <td>18.35</td>\n",
       "      <td>...</td>\n",
       "      <td>11.38</td>\n",
       "      <td>10.95</td>\n",
       "      <td>10.62</td>\n",
       "      <td>9.96</td>\n",
       "      <td>9.44</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>14.63</td>\n",
       "      <td>16.58</td>\n",
       "      <td>19.41</td>\n",
       "      <td>20.59</td>\n",
       "      <td>20.63</td>\n",
       "      <td>19.12</td>\n",
       "      <td>20.19</td>\n",
       "      <td>20.7</td>\n",
       "      <td>20.84</td>\n",
       "      <td>20.03</td>\n",
       "      <td>...</td>\n",
       "      <td>11.41</td>\n",
       "      <td>11.42</td>\n",
       "      <td>10.88</td>\n",
       "      <td>10.36</td>\n",
       "      <td>9.87</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.3</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>14.63</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.05</td>\n",
       "      <td>19.28</td>\n",
       "      <td>18.74</td>\n",
       "      <td>17.18</td>\n",
       "      <td>18.5</td>\n",
       "      <td>19.4</td>\n",
       "      <td>19.65</td>\n",
       "      <td>18.3</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.95</td>\n",
       "      <td>10.6</td>\n",
       "      <td>9.87</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.02</td>\n",
       "      <td>8.88</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>14.63</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.05</td>\n",
       "      <td>20.06</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.75</td>\n",
       "      <td>18.55</td>\n",
       "      <td>20.56</td>\n",
       "      <td>20.05</td>\n",
       "      <td>20.03</td>\n",
       "      <td>...</td>\n",
       "      <td>11.28</td>\n",
       "      <td>11.42</td>\n",
       "      <td>10.88</td>\n",
       "      <td>10.36</td>\n",
       "      <td>9.87</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>16.25</td>\n",
       "      <td>18.05</td>\n",
       "      <td>20.06</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.75</td>\n",
       "      <td>18.55</td>\n",
       "      <td>20.56</td>\n",
       "      <td>20.05</td>\n",
       "      <td>20.03</td>\n",
       "      <td>18.21</td>\n",
       "      <td>...</td>\n",
       "      <td>11.42</td>\n",
       "      <td>10.88</td>\n",
       "      <td>10.36</td>\n",
       "      <td>9.87</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.1</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.82</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>-9.9692</td>\n",
       "      <td>-9.9723</td>\n",
       "      <td>-10.0199</td>\n",
       "      <td>-2.763</td>\n",
       "      <td>10.0267</td>\n",
       "      <td>1.0782</td>\n",
       "      <td>-9.7763</td>\n",
       "      <td>2.5436</td>\n",
       "      <td>0.0999</td>\n",
       "      <td>9.9945</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5.02</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-3.47</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>35453.0</td>\n",
       "      <td>63387.0</td>\n",
       "      <td>252984.24</td>\n",
       "      <td>319186.05</td>\n",
       "      <td>366653.18</td>\n",
       "      <td>143819.77</td>\n",
       "      <td>166664.47</td>\n",
       "      <td>237036.21</td>\n",
       "      <td>351013.04</td>\n",
       "      <td>143054.98</td>\n",
       "      <td>...</td>\n",
       "      <td>31608.86</td>\n",
       "      <td>51316.44</td>\n",
       "      <td>16789.06</td>\n",
       "      <td>21279.37</td>\n",
       "      <td>28143.77</td>\n",
       "      <td>13111.33</td>\n",
       "      <td>17904.55</td>\n",
       "      <td>24813.1</td>\n",
       "      <td>32126.76</td>\n",
       "      <td>37140.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>51867.739</td>\n",
       "      <td>103125.206</td>\n",
       "      <td>466472.485</td>\n",
       "      <td>638201.85</td>\n",
       "      <td>751937.656</td>\n",
       "      <td>258315.553</td>\n",
       "      <td>322013.478</td>\n",
       "      <td>480549.269</td>\n",
       "      <td>710849.839</td>\n",
       "      <td>283291.718</td>\n",
       "      <td>...</td>\n",
       "      <td>35474.6542</td>\n",
       "      <td>58441.3395</td>\n",
       "      <td>18214.4604</td>\n",
       "      <td>21673.0221</td>\n",
       "      <td>27243.276</td>\n",
       "      <td>12122.3881</td>\n",
       "      <td>16210.6835</td>\n",
       "      <td>22488.1075</td>\n",
       "      <td>29186.3041</td>\n",
       "      <td>35341.7808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3389 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0           1           2          3           4     \\\n",
       "ts_code     000004.SZ   000004.SZ   000004.SZ  000004.SZ   000004.SZ   \n",
       "trade_date   20241225    20241224    20241223   20241220    20241219   \n",
       "open            14.63       16.25       18.11      19.62       18.75   \n",
       "high            14.63       16.58       19.41      20.59       20.63   \n",
       "low             14.63       16.25       18.05      19.28       18.74   \n",
       "close           14.63       16.25       18.05      20.06       20.63   \n",
       "pre_close       16.25       18.05       20.06      20.63       18.75   \n",
       "change          -1.62        -1.8       -2.01      -0.57        1.88   \n",
       "pct_chg       -9.9692     -9.9723    -10.0199     -2.763     10.0267   \n",
       "vol           35453.0     63387.0   252984.24  319186.05   366653.18   \n",
       "amount      51867.739  103125.206  466472.485  638201.85  751937.656   \n",
       "\n",
       "                  5           6           7           8           9     ...  \\\n",
       "ts_code      000004.SZ   000004.SZ   000004.SZ   000004.SZ   000004.SZ  ...   \n",
       "trade_date    20241218    20241217    20241216    20241213    20241212  ...   \n",
       "open             18.37        20.0       19.88       20.03       18.35  ...   \n",
       "high             19.12       20.19        20.7       20.84       20.03  ...   \n",
       "low              17.18        18.5        19.4       19.65        18.3  ...   \n",
       "close            18.75       18.55       20.56       20.05       20.03  ...   \n",
       "pre_close        18.55       20.56       20.05       20.03       18.21  ...   \n",
       "change             0.2       -2.01        0.51        0.02        1.82  ...   \n",
       "pct_chg         1.0782     -9.7763      2.5436      0.0999      9.9945  ...   \n",
       "vol          143819.77   166664.47   237036.21   351013.04   143054.98  ...   \n",
       "amount      258315.553  322013.478  480549.269  710849.839  283291.718  ...   \n",
       "\n",
       "                  3379        3380        3381        3382       3383  \\\n",
       "ts_code      000004.SZ   000004.SZ   000004.SZ   000004.SZ  000004.SZ   \n",
       "trade_date    20100303    20100302    20100301    20100226   20100225   \n",
       "open             11.38       10.95       10.62        9.96       9.44   \n",
       "high             11.41       11.42       10.88       10.36       9.87   \n",
       "low               11.0       10.95        10.6        9.87       9.35   \n",
       "close            11.28       11.42       10.88       10.36       9.87   \n",
       "pre_close        11.42       10.88       10.36        9.87        9.4   \n",
       "change           -0.14        0.54        0.52        0.49       0.47   \n",
       "pct_chg          -1.23        4.96        5.02        4.96        5.0   \n",
       "vol           31608.86    51316.44    16789.06    21279.37   28143.77   \n",
       "amount      35474.6542  58441.3395  18214.4604  21673.0221  27243.276   \n",
       "\n",
       "                  3384        3385        3386        3387        3388  \n",
       "ts_code      000004.SZ   000004.SZ   000004.SZ   000004.SZ   000004.SZ  \n",
       "trade_date    20100224    20100223    20100222    20100212    20100211  \n",
       "open               9.1         9.1        9.09         9.1         9.5  \n",
       "high              9.45        9.22         9.3        9.34        9.94  \n",
       "low               9.02        8.88        8.82        9.03         9.5  \n",
       "close              9.4         9.1        9.22        9.17         9.5  \n",
       "pre_close          9.1        9.22        9.17         9.5        10.0  \n",
       "change             0.3       -0.12        0.05       -0.33        -0.5  \n",
       "pct_chg            3.3        -1.3        0.55       -3.47        -5.0  \n",
       "vol           13111.33    17904.55     24813.1    32126.76    37140.43  \n",
       "amount      12122.3881  16210.6835  22488.1075  29186.3041  35341.7808  \n",
       "\n",
       "[11 rows x 3389 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x261181fa540>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./stock000004.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3359 entries, 0 to 3358\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3359 non-null   datetime64[ns]\n",
      " 1   open        3359 non-null   float64       \n",
      " 2   high        3359 non-null   float64       \n",
      " 3   low         3359 non-null   float64       \n",
      " 4   close       3359 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 131.3 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3354</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>18.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.74</td>\n",
       "      <td>20.63</td>\n",
       "      <td>19.708</td>\n",
       "      <td>19.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>19.62</td>\n",
       "      <td>20.59</td>\n",
       "      <td>19.28</td>\n",
       "      <td>20.06</td>\n",
       "      <td>19.710</td>\n",
       "      <td>19.325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>18.11</td>\n",
       "      <td>19.41</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.05</td>\n",
       "      <td>19.208</td>\n",
       "      <td>19.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.748</td>\n",
       "      <td>19.114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>17.924</td>\n",
       "      <td>18.756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10\n",
       "3354 2024-12-19  18.75  20.63  18.74  20.63  19.708  19.168\n",
       "3355 2024-12-20  19.62  20.59  19.28  20.06  19.710  19.325\n",
       "3356 2024-12-23  18.11  19.41  18.05  18.05  19.208  19.307\n",
       "3357 2024-12-24  16.25  16.58  16.25  16.25  18.748  19.114\n",
       "3358 2024-12-25  14.63  14.63  14.63  14.63  17.924  18.756"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mplfinance\n",
      "  Downloading mplfinance-0.12.10b0-py3-none-any.whl.metadata (19 kB)\n",
      "Requirement already satisfied: matplotlib in e:\\w\\lib\\site-packages (from mplfinance) (3.8.4)\n",
      "Requirement already satisfied: pandas in e:\\w\\lib\\site-packages (from mplfinance) (2.2.2)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in e:\\w\\lib\\site-packages (from matplotlib->mplfinance) (1.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in e:\\w\\lib\\site-packages (from matplotlib->mplfinance) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in e:\\w\\lib\\site-packages (from matplotlib->mplfinance) (4.51.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in e:\\w\\lib\\site-packages (from matplotlib->mplfinance) (1.4.4)\n",
      "Requirement already satisfied: numpy>=1.21 in e:\\w\\lib\\site-packages (from matplotlib->mplfinance) (1.26.4)\n",
      "Requirement already satisfied: packaging>=20.0 in e:\\w\\lib\\site-packages (from matplotlib->mplfinance) (23.2)\n",
      "Requirement already satisfied: pillow>=8 in e:\\w\\lib\\site-packages (from matplotlib->mplfinance) (10.3.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in e:\\w\\lib\\site-packages (from matplotlib->mplfinance) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in e:\\w\\lib\\site-packages (from matplotlib->mplfinance) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in e:\\w\\lib\\site-packages (from pandas->mplfinance) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in e:\\w\\lib\\site-packages (from pandas->mplfinance) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in e:\\w\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->mplfinance) (1.16.0)\n",
      "Downloading mplfinance-0.12.10b0-py3-none-any.whl (75 kB)\n",
      "   ---------------------------------------- 0.0/75.0 kB ? eta -:--:--\n",
      "   ----- ---------------------------------- 10.2/75.0 kB ? eta -:--:--\n",
      "   ---------------- ----------------------- 30.7/75.0 kB 445.2 kB/s eta 0:00:01\n",
      "   --------------------- ------------------ 41.0/75.0 kB 330.3 kB/s eta 0:00:01\n",
      "   ---------------------------------------- 75.0/75.0 kB 462.7 kB/s eta 0:00:00\n",
      "Installing collected packages: mplfinance\n",
      "Successfully installed mplfinance-0.12.10b0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install mplfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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IiIiIiIiISJUUsIqIiIiIiIiIiIhUSQGriIiIiIiIiIiISJUUsIqIiIiIiIiIiIhUSQGriIiIiIiIiIiISJUUsIqIiIiIiIiIiIhUSQGriIiIiIiIiIiISJUUsIqIiIiIiIiIiIhUSQGriIiIiIiIiIiISJUUsIqIiIiIiIiIiIhUSQGriIiIiIiIiIiISJUUsIqIiIjsxD7zmc9gjKn4+PWvf13YbvXq1ZxzzjnsuuuuRCIRRo0axT/+4z+yatWqfp0/nU7z/e9/n3333Zeamhp22WUXTjrpJJ5//vmK27e1tXH77bfz5S9/mXPPPZd/+7d/o6WlpV9jEBERERHZkYy11u7oQYiIiIhIdSZPnkwkEuFzn/tc2brPfe5zTJ8+nXXr1jF79mxWrFjBLrvswowZM3jllVf44IMP2GuvvVi0aBH19fVVnf/jH/84f/rTn/jEJz7BXnvtxXvvvcdDDz0EwP/93//R3Nxc2HblypWccMIJvPHGG+y22274vs/q1auZMGECjz76KAcccEB1b4KIiIiIyA6kgFVERERkJ7V+/XrGjRvH17/+dW6++eYutzvzzDO56667uPzyy/nBD35AJBIhnU5z0kknMW/ePG666SYuvPDCPp9/3rx5HH/88fz4xz/mu9/9bmH5X//6V4488kiOOeYYnnjiicLy4447jr/97W889NBDHHvssQD8+te/5h//8R9pbm5m3rx5fR6DiIiIiMiOphYBIiIiIjupF198EYDZs2d3uc3mzZt58MEH+cxnPsOll15KJBIBIB6Pc9FFFwHwt7/9rV/nP+mkk0LLjzjiCJqamli6dGlh2bx585g/fz5XXHFFIVwFOP3005k1axZPPfUUvu9XNQ4RERERkR0psqMHICIiIiLVeeGFFwA48sgju9ymoaGBzZs3k8lkytZ5ngdANBqt6vwdbQVee+210O39K1euZOPGjRxxxBGFZYcddhjPPvssM2fOLDvO5s2bSSQSuK5b1ThERERERHYkVbCKiIiI7KReeOEFYrEY3/ve95gwYQI1NTUceOCBXHfddaTT6cJ2rutSU1NTtn9Hr9Q5c+ZUdf5TTjmFuro6vvWtb/H444/T3t7Oq6++yumnn461lvPPP7+w7YgRIzjiiCOora0NHeP+++/n3Xff5ZRTTqlqDCIiIiIiO5p6sIqIiIjshKy1jBkzho0bNzJp0iROOOEEXNflz3/+M8uXL+fDH/4wjzzyCMaYivsvW7aM/fffn4aGBt59992KAWxvPP3005x88sls2bKlsKy+vp5bbrmFs846q+I+qVSK++67j0ceeYR7772XWbNm8fDDD9PU1FTVGEREREREdiS1CBARERHZCW3YsIGZM2dy4IEHcuONNxZury+evOrPf/4zH/vYx8r2tdbyxS9+kXQ6zU9+8pOqw1Xf97nlllvYsmULjuMwceJEWlpaaG1t5fbbb+eYY45hr732KtuvpaWFr371q7S2tmKM4dxzz1W4KiIiIiI7LbUIEBEREdkJjR07lscee4x/+7d/C/Uujcfj/PSnPwXgrrvuqrjv1Vdfzfz58/nEJz7BF77wharH8JOf/IT777+fz372s6xdu5aVK1eyYcMGfvjDH/L0009z4oknVpy4atddd2XLli08/fTTzJkzh6985Sv88Ic/rHocIiIiIiI7kgJWERERkb8z+++/PwBLly4tW/eXv/yFH/3oR+y5557813/9V7/O86tf/YqxY8dy1113MWbMGADq6uq44oor+MxnPsPSpUt59tlnK+7rOA5HHXUUf/7zn9ljjz346U9/ysaNG/s1HhERERHZfu644w6MMXz84x8vLPvZz36GMSbUKuoPf/gDxhgee+yxsmP84Ac/wBgTehx++OHbY/gDSgGriIiIyE7qnXfeqbh8w4YNQK7XabE333yTM844g0Qiwe9+9zsaGxv7df7333+fKVOmEI/Hy9ZNnz4dgJUrVxaWVWr9X1dXR3NzM+l0mmXLlvVrPCIiIiKy/S1atKjw8+LFi8vWP/LIIwA8/PDDZesWLFjAeeedx4svvlh43HHHHYM21sGigFVERERkJ3TZZZcxderU0AVthz/+8Y8AzJgxo7Bs1apVfPSjH2Xz5s3ceeedzJo1q99jGDduHO+88w5BEJSt+9vf/gZQqGy95pprqK+vrxiitrS0ALnJsURERERk5/L++++zefNmgIrXpg8//DDxeLwQtBZbsGABxx9/PLNnzy489t1338Ee8oBTwCoiIiKyE+qYvOrrX/86yWSysPxvf/tboZ/pueeeC+QqWo877jjeffddrrrqKj796U8PyBhmzpzJ+vXrufTSSwvVsqlUihtuuIE//vGPNDQ0cNRRRwFw9NFH097eztlnn01bW1vhGC+++CJ//vOf2Xvvvdlnn30GZFwiIiIiQ5m1liCVHHKPSncb9WTSpElMnDiRxYsXk81mWbJkCR/60IcK65cvX87SpUu58MILWbBgAevXry+sW7VqFWvXruXQQw8dkPd1R4rs6AGIiIiISN8ddthhnH322fzqV79i8uTJzJgxg23btvHCCy8QBAHf//73Of744wH4/ve/z5tvvklTUxPt7e1ccskloWM1NjbyrW99q/C8Y/1VV13V7Rguv/xyHnvsMa6++mp+9rOf0djYyIYNG/A8D4Drr7+e2tpaIBewXnDBBfziF79gv/3246STTqK9vZ0HHniARCLBr371K4wxA/b+iIiIiAxVNp1i228rT0a6I4047QuYRE2f95s+fTqLFy+moaEBYwx77713Yd0jjzxCIpHg29/+Ntdffz2PPvooZ5xxBgAvvfQSkUiEz33ucyxatIimpibOOussLr30UqLR6IC9ru1BAauIiIjIzqa5GYD/sJbZt97KL37xC5588klGjRrFKaecwle/+tVCuArwxBNPALlb8a+++uqyw02aNCkUsHZs01PAevDBB/PSSy9x9dVXM3/+fNauXUtNTQ0zZ87koosu4pOf/GRo+1tuuYWDDz6YX/ziF9x99900NTXxmc98hssuu4w999yzqrdCRERERHas6dOns2jRIhoaGth///1xXbew7uGHH+bwww9n7NixzJo1i0ceeaQQsL7wwgvE43E++clPctVVV/Hiiy9y6aWXAnDllVfukNdSLQWsIiIiIjsp1xguuOACLrjggm63e+utt/p03L7cHrbvvvty991392pbx3E499xzC60LRERERGTnN336dH7xi1/Q0NAQmgMgCAIee+wxvva1rwEwd+5c7rvvvsL68847jzPPPJOpU6cC8OEPf5i2tjb+/d//ncsvvxzH2Xk6m+48IxUREREREREREZEhZfr06bz66qssXLiQ6dOnF5a//PLLtLS0cNlll2GM4frrr2flypW8/vrrAOy6666FcLXDUUcdxfr16/nggw+262voL1WwioiIiIiIiIiIbCcmnmDEaV/Y0cMoY+KJqvbbf//9SafTPProo3znO9/hlVdeAXLtAerq6njqqacwxuB5HkceeSSPPPII+++/Py+88AKTJk1i/PjxhWNt3LgRoDCB6s5CFawiIiIiIiIiIiLbiTEGJ1Ez5B7VTjiaSCQKlajFLQIeeeQRjjjiCA466CBmzZrF7NmzOeSQQ3j44YcBuOCCC/j5z38eOtbdd9/N2LFjmTJlSpXv7o6hClYRERERERERERGp2vTp02lpaWHChAkAtLe38+yzz3LxxReHtjvmmGO49dZbyWQyfPWrX+XLX/4yrusyefJkHnzwQf7v//6PW2+9dafqvwoKWEVERERERERERKQfOgLWDk888QSZTIY5c+aEtjvmmGP4yU9+wjPPPMNZZ53Fpk2buOGGG9iwYQMHHngg//u//8tHP/rR7T38fjO2L9PEioiIiMiO19zc+fP8+TtuHCIiIiIioh6sIiIiIiIiIiIiItVSwCoiIiIiIiIiIiJSJQWsIiIiIiIiIiIiIlVSwCoiIiKyE2qeuTD3uLO5541FRERERGTQKGAVERERERERERERqZICVhEREREREREREZEqKWAVERERERERERERqZICVhEREREREREREZEqKWAVERERERERERERqZICVhEREREREREREZEqRQbrwO3t7Zx99tlYa7vc5rjjjuO8884LLUsmkzz44IM899xztLS00NjYyLHHHssnP/lJotHogI9zzZo1/OY3v2Hx4sW0t7ez6667csopp3D00Ud3u9+yZcv47W9/yxtvvEEmk2Hy5MmcdtppzJo1a8DHKCIiIiIiIiIiMpTccccdnH322Zxyyin88Y9/BOBnP/sZ3/nOdzjzzDO54447APjDH/7AJz7xCebNm8dxxx1X8ViXXHIJK1euLOxT7O677+byyy9n48aNfPazn+Wmm24ikUgM1suqyqBVsL799ttYazHGEI1GKz5c1w3t09bWxqWXXsof/vAH1q5di+/7rF+/ngceeIAf//jHBEEwoGN89913+d73vsdTTz3Fli1b8DyP9957j1tuuYUHHnigy/0WLlzIxRdfzIsvvkhrayvZbJY333yTa6+9lscff3xAxygiIiIiIiIiIjJULVq0qPDz4sWLy9Y/8sgjADz88MMV97///vu55pprKq773e9+xxe+8AVOPPFE7r33XhYuXMg3v/nNARj1wBq0Cta3334bgBNOOIFzzz23V/vcfPPNLF++nHg8zrnnnsucOXPYsmULt912G4sXL+ZPf/oTH//4xwdkfG1tbVx77bUkk0nGjx/PV77yFaZNm8ayZcu48cYb+c1vfsPMmTPZe++9Q/utWbOG66+/Ht/32WuvvTj//PPZfffdeeWVV7jpppu4/fbb2X///Rk3btyAjFNERERERERERP5+WGtJZQe2iHAgJKIOxpg+7/f++++zefNmGhoaQmFrh4cffph4PM4jjzzCddddF1r3y1/+km9961vsv//+FY998cUX89GPfpSf//znAEydOpV9992Xyy67jPHjx/d5rINl0ALWpUuXArDXXnv1avsFCxawcOFCAM4//3yOPPJIAJqamrjooov4yle+wv33389xxx1HfX19v8f3u9/9js2bNxOPx7nkkksK/1KmTp3KV7/6VX70ox9xxx13cPXVV4f2u/fee0mn0zQ2NnLJJZcUxjJz5kzOPvtsfv7zn3PPPffwjW98o99jFBEREamkecrTLKzxAZi1Y4ciIiIiIn2Uygbc/eLyHT2MMv986CRqYm7PGxaZNGkSmUyGxYsXc8QRR7BkyRI+9KEPFdYvX76cpUuX8t3vfpef/vSnrF+/nrFjxxbWP/fcc8ybN49f/OIXZcdetWoVb7zxBhdffHFh2ZQpU9h///157LHHOOOMM6p4lYNj0FoEdASs06ZN69X2HWXCkydPLoSrHWpra/nwhz9MJpPh5Zdf7vfYfN9n3rx5ABx//PFlifd+++3H1KlTWbp0KRs2bCgs37p1K88//zwAp556alnQe9RRR9HY2MhLL71ENpvt9zhFRERERERERESGsunTp7N48WLeeOMNjDGhu8EfeeQREokE3/72t3Ech0cffTS073/+539y2GGHVTzuqlWrAJgxY0Zo+R577FHIHYeKQQlY16xZw9atW2loaGDXXXft1T5LliwB4Igjjqi4/qCDDgIoVLn2x4oVK2hra+v2fB2TVRWfb8mSJYU+sJX2c12XGTNmkE6neeONN/o9ThERERERERERkaFs+vTpLFq0iMWLF7P//vuH5lx6+OGHOfzwwxk7diyzZs0q9GPt4DhdR5PJZBKAxsbG0PKamhrWr18/gK+g/walRcBbb70FwIgRI7jpppt466232LJlC3V1dUybNo3jjjuOgw8+uLD91q1bC4FnVxWvkyZNAuCDDz7o9/hWr14NQCQSYcqUKd2eryMth1xwDDBu3Liyf7mVxlmasPekpaWlx20aGhrKJgcTERGR4aXV9fGN3dHDEBERERFh+vTp/OIXv6ChoSGUhQVBwGOPPcbXvvY1AObOnct9993X6+PG43GAshwsFosVwtehYlAC1jfffBPIVYp+8MEHTJo0iYkTJ/LBBx/wwgsv8MILL3D88cfz5S9/GYBt27YV9t1ll10qHrO+vh7XdVm3bl2/x9dxvnHjxnUZVo4aNQogdL6O/SZOnNjlsUeOHFm2X2+df/75PW5z22230dTU1Odji4iIiIiIiIjIjpeIOvzzoZN29DDKJKLV3eg+ffp0Xn31VUaOHMmJJ57IK6+8AsDLL79MS0sLl112GZdddllh+9dff73LSa2KdUwgv2rVqlAWt3HjxrJJ6Xe0QQlYO273P+igg/jiF79YaF4bBAGPPvoot99+O48++igHHHAARx11VKhfaW1tbZfHra2tZdu2bWSzWaLRaNXj8zwPgLq6ui636Vi3adOmwrKOcXa3X0df1uL9RERERAZae/5SaOGahTt0HCIiIiLSN8aYPk8mNZTtv//+pNNpHn30Ub7zne8UAtaHH36Yuro6nnrqKYwxeJ7HkUceySOPPNKrgHWPPfZg4sSJPPPMMxxyyCEAWGt5+eWXOfbYYwf1NfXVoASs//Iv/8LGjRuZPXs2kUjnKRzH4YQTTmDDhg38/ve/56GHHuKoo44q9FswxhCLxboebP5YmUymXwFrx/l6e67+7tdbt912W4/bNDQ09Pm4IiIi8velJmtBHQJEREREZAhIJBJMnTqVN998kxkzZnDPPfcAuQmujjjiiMK8SgCHHHIIDz/8MBdeeGGPx3Uch0996lPceuutnHPOOdTX13Pfffexdu1ajj/++EF7PdUYlEmupk2bxuGHHx4KV4sdd9xxACxbtoytW7cWeip019i2eH06ne7X+DoC0u56mRpjgHBQ2lXvh572662mpqYeH+q/KiIiIsVaM607eggiIiIiMsxNnz6dMWPGMGHCBADa29t59tlnOfroo0PbHXPMMTzxxBO9zs2++93v0tLSwuzZs/nnf/5nzjzzTE499dRCRetQMSgBa0+Ke4iuW7eucFu97/ts3bq1y/1aWwfmD4je3MbfMemWtZ3lIdXuJyIiIjKQRnkuH9vUwMc2NdCQ1ZevIiIiIrJjTZ8+nenTpxeed4Soc+bMCW13zDHH0NbWxjPPPNOr4+6xxx689NJLHHLIIbz22mt861vf4t577x3QsQ+EQWkRAHTbJ7U0RK2rq6OmpoZkMsm6desKE0UVS6VShcrVRCLRr7GNGTMG6H4iqs2bN5edq9r9RERERAaKtZYPpUezKZm7jGvyY1hrC3fRiIiIiIhsD2eddRZnnXUWAJdeeimXXnopAHfccUeX+5x00kkVixK722fSpEn8z//8T3+GOugGvIL1+eef5/zzz+dXv/pVl9u89tprQO52+o7S4b322gvItQ2o5K233gKgpqam24mwemP33XcnFouRTqf54IMPKm6zdOlSIFxtO2XKFABWr15NMpnsdpzF+4mIiIgMhOY7mzn1l3Opt53fkTdmI9g2tQkQEREREdlRBjxgbWxspKWlhWeffbbi7f6e5/H73/8egAMOOKBw2/2MGTMAePbZZyset2MGssmTJ/d7jNFolP322w+gy5LkSucbM2YMEydOxPd9nn/++bJ9giAohMcdgbGIiIjIgGhuhgULqV30RmixYwzWy+6gQYmIiIiIyIAHrNOmTWOfffahvb2dm266KRSybt68mWuvvZaVK1fiui6nn356Yd3RRx+N4zi8/vrrvPzyy6Fjbt26lXnz5gFw8MEHD8g4jz32WAD+/Oc/s3HjxtC6V199tVDBWnq+jv0eeOABUqlUaN3jjz/Opk2bcByHmTNnDsg4RURERIrVBOWtAGy275NrioiIiIjIwBiUSa6+8pWv0NTUxKuvvsp5553Hd7/7Xb7zne9wwQUX8Morr1BXV8dFF13EtGnTCvuMGTOGI488EoCbbrqJp556ikwmw7Jly7j66qtpbW2ltraW5ubm0LnWr1/P5z//eT7/+c/z5JNP9nqMhx9+OOPHj6etrY0rrriCJUuWkMlkeOGFF7jhhhsA2HfffZk6dWpov+OPP566ujrWrVvHlVdeyfLly0mlUjz22GPcfvvtABx11FE0NjZW9d6JiIiIdCdqyy/fbFYVrCIiIiIiO4qxgzTdfWtrK7///e/561//ysaNG0kkEuy6664cdNBBnHDCCYwYMaLiPpdffjnLly8vH6gxfP3rX+eoo44KLV+3bh1f/epXAbjggguYO3dur8f4zjvvcNVVV9HW1la2rq6ujquvvppddtmlbN3LL7/M9ddfT7bCHzNjx47lmmuuYdSoUb0eh4iIiEiPmptpnrmQ/dtr2HszrMjPCWoch2svfoLopCk7dHgisvNpvrOzeGX+mfN34EhERER2boMWsFYrlUpx7733Mm/ePDKZ3O1uEydO5Mwzzxyw9gDF1qxZwx133MGCBQsKs5gdcMABfOlLX6oYrnZYtmwZd9xxB0uWLAFyAfChhx7Kueeeq+pVERERGXj5gHVGWy2Tt9hQwHrNdx8lNnXfHTs+EdnpKGAVEREZGEMuYO2QTCZZuXIlNTU17LbbboN+vs2bN7Nu3Tqamppoamrq9X4bNmxg48aNjB8/XlWrIiIiMnjyAeshrXXstjVgxUj4wiLAGPYdMYX4vN63ShIRAQWsIiIiAyWyowfQlZqaGvbee+/tdr6GhgYaGhr6vN+YMWMYM2bMwA9IREREpIKIrTDJFUPy+3IRERERkWFhUCa5EhEREZHBEa0YsIqIiIiIyI4yZCtYRURERKRcxILnGKKBxZDrA4/ZvhGrbisWEREREemkgFVERERkJxK1DgGWjbX7cuvh4xm37XWy7ntct6MHJiIiIiIyTKlFgIiIiMhO5FOLM7zXdCyba/chFW3g/dFH0h6btKOHJSIiIiIybKmCVURERKS585Z35g/RW94XLoQpraSdUey9qYFUFHwDGNhUdyDW2ly7ABERERER2a4UsIqIiIjsJBzPJxmrLVtuTZy2jE99XJd2ItJ7C9cs3NFDEBER+bugFgEiIiIiO4kIhmS0pmy5iyGV9XfAiERERERERGUOIiIiIjuB5tNaAUN7hYDVsZDygkEeQFEbhbMG91QiIiIiIjsTBawiIiIiO4mIrVzBan2fVDoLlK8bcAsXwoL8zwfNGvzziYiIiIgMcQpYRURERIDmmQtzP9zZzPwzh+ZEVxHrkIokKq5LJtPbbRytmVYA6rfbGUVEREREhi4FrCIiIiJ0hoZvD9FJX1pdnxG+S1usLvc8BjVe5/qM53Wx58B46v2nATh8i0cilVumCXJERERERBSwioiIiOw0IrhsrmkgVqHdanY7TnL1yvjcP9vyobSIiIiIyHDm7OgBiIiIiOxwCxeC7+ceQ1jU1PD62AjtFb4iz3rbd+ytUfADn+Y7m3veWERERETk75gqWEVERER2EhHiXa7LDHbAOsTDZxERERGRHUUBq4iIiMhOIkoc28W6Qa1gbW6mJpM7sxm8s4iIiIiI7JTUIkBERERkJxE1nRWsNVkoTlsHvYIVmNYCjoWIb9g1HaPG16WkiIiIiIgqWEVERER2AsbCeL+eFUXLklEIHIPruHh+hZmvBkHgOvxjyxiixiUdgfd33T7nFREREREZqlR2ICIiIrIT2DdTh+9Eu1y/vQLWbEMd9YGLARKeYfImBawiIiIiMrypglVERERkJzAjNZL1deGA1TcAFj/wt1vAmh5dT8aFcW25czsLl22X84qIiIiIDFWqYBURERHZSRy2Kso+LVDrgVsy25UfDH7AunSci4UuJ9oSERERERmOFLCKiIiI7CQ8p+ubj7ZHBasTyZ0/wGVLYldaY2MJDFiryFVEREREhi+1CBAREREZ4qznAZB1Y11u4/mDH3I6bgQLbKw/kg9iYwGozawiSKdwEzWDfn4RERERkaFIFawiIiIiQ5zNpADIFlWwZky4YnV7tAgg4vLMnrNJ5sNVgPbYLixf1TL45xYRERERGaIUsIqIiIgAS5osCyZYNqc203xnc+93bG7ufAySzgrWzkmu/JJOqDYI8IPBrWJtadiFZaP3AKDGyz1GZA3vrt86qOcVERERERnKFLCKiIiIDHX5gDUVSeRiVQuB6QxTLRZr7aAGrA6GP82YBEB9BjpO72PZtC01aOcVERERERnqFLCKiIiIDHHW93ADSyra0efUhupXDQasxRukNgEWS82YMbQVtQboPLlhU3taE12JiIiIyLClgFVERERkqPM8AhPF6+jBaqEszrSWwSpg9Y3FRCJEgkzF9dmMR3Y7TLIlIiIiIjIURXreRERERES60jxzYeHn+YN0Dut7+E5n/1VTqVrUDl4PVj/fD8Bz412Orz3rE4vou3sRERERGX4UsIqIiIgMcdbzsETDy0zpRpZgEG/Tt4Dn5ALWmWtg8QTwTX4Qnkcy49FQkxtj8SRh888crNhZRERERGRoUMAqIiIiMtR5Hp98K8b/7Q2tMcBaTJDBsS6BcXAdFwZ5kqsAg+/ECs8tkEy4uZ/zFawFCxZ2/nzmoA1JRERERGRIUMAqIiIiMsRZL4vnuEULAOthbEBHS307iD1YLZasG8NSWjbbMT6PtBeeYKs1uQWAxkuizNp7DqBqVpGh6I+/bM398H/NMF+/oyIiItVQoywRERGRajU342/bgr9tC62Z1kE7jU22k3Vzt9+/Mxpaoxbf8TAUhZqD2CLAGkhHcu0BAif3CPFLAtbWVrCW2x6yTF3vhytaRURERET+zihgFREREQEev8vhxBVxPrRq6F0eBcl2sm6E1hj4BqwNiipY8waxRUCAJR3pbA9gsh4AfuDjBz4LPniJZMYr2++8k+HNJsvC+sELn0VEREREdjS1CBAREZFhz4u51I0dx7HthjbH57ns4PUyrYZNtpF1Oi/bntnNkq41jEsWjXMQK1iDD1aQHT8WgPZILlg1Jprr/QoYIJ0tD1hjgeHQ1jpwXFx/aL2nIpJz4Qm5/smj4gtRgwAREZHqDL0SDREREZHtLFOfAJPrL1oXuBy4tjws3JFsNkvWdAasvrGMqxtNpGOCK8hVsA5WiwDHKbQIeKsJntjDMnPaMaFtUqls7ofmZvB9agOHg8xYPrJ+BB9ZW8vsp98ZlLGJSPWmLtuC41scfQEiIiLSLwpYRUREZNjL1nbe/m4NxFau2oGjKWc9Dy+SC1intcDRy+Gcg84s78EadHGA/jLw1B4xklFIRqFlVJRFaxcVWgS0ZlpJ51sEWCx7eDWc3DqeOA0sGz2ND0aOBRsQtKlVgIiIiIj8/VGLABERERGzowfQg8AvTHIFYC3EIk4oYLWD1CLAWot1DEe/H2ftSPAd8BxwrI8ldz4/8EllslhraXM9DkqPYltsBKsamgnylbdjty3EW7+GWN3UAR+jiIiIiMiOpApWERERkUxmaGesvkcmnihaYIm6TskkV8HgTHLl5SpTAyfXIsBYaKobR5T20GapjEeweSP/1fQeq+sNqxoOLoSrABtGzGJ1y9aBH5+IiIiIyA6mgFVERESkP6zNPXx/8E7heXhO+MajqOsARYFqMEgVrF6ut2rGjWEAF0NgIGrDAWs66+G1rAcgYnenPdoUPg7w9Mo27CD1iRURERER2VHUIkBERERkqPN9skUBqyHfIsCW9GAdhOyyI2BNRcJ9al2SQLywLPB90u1JALYlJgJQk+08jjGGze0Z2jI+9XFdgoqIiIjI3w9VsIqIiMiwNtQrKq212NtvJ1s8zGiMaMQNT3KFHdQWAel8wGqBwECkpEUAQUAqlSZjLKnIyIqHspkMLW2ZgR+jiIiIiMgOpIBVREREhrfsEA/8Ah8MoUmuwJYHrIM1yVWhgjWeP3PuHMakwhW0QUAyneWktyyBk2CfFnBLhmPTKTa2pQd8jCLSP2+OgYX1rTTf2byjhyIiIrJTUsAqIiIiw1rQ1lpx+ZCpbPV9WLOGrInwzmh4p8nw6OHjy1oE2EEKWPF9fGPIurnb+gtnND6OzYWlFosNfNJZD9+JEJjOMDgZhSmbYK+Nlm3PPcGmbcmBH6OIVKUmazHk2o4MZh9pERGRv3cKWEVERGRYC9orB6wEQyNssPnQw3PdzmVA1I1UqGAdhPMHAZmit6KjgvXbR12IG3RWo/75rf/Hzc/+nPZIAoAaDzY6GUZkcuFNJN9DdmubAlYRERER+fuigFVERESGNZtMQixGWTYZBJU23/58nwATmuTKGohGHSiuWA2CwenB6vuF/qsd5wZIxKM4Qa69wsw14LRsxK7eRCqaKGzrGUtQ8s62tatFgIiIiIj8fVHAKiIiIsOazaQqLx8it8taG+A5bngZEN9ePVh9LxywAsb4uJEIru0MS43ngeeQiiTw8yFsgMUvDVhTmaHTfkFEREREZABEet5ERERE5O+XTXdRUTlEWgQQBIX+px2sgUi0UouAQapgdYsDVgsmi4lEQy0CHGs4YmWcVLQGgPYIfORtGEUbqxpGdh4ukyHrW2IRM/BjFRERERHZAVTBKiIiIsPa6fd+ilXRNFnHZUPd3qyv3wffxIZMBSu+H24PkP9nLOqGJrnCWvzB6GoQBKEK1gAwJouJRHCCzurfg1dbYkGCZ/ZIkIxC4MDmuKUx086bTZY3GzzebPCwvkdmUAYqIiIiIrJjqIJVREREhrVYYLBYnt/jcNaMHA8GbGSPIROw2iDACwWsFmMCXCfcIsAOWouAzgrWmWvAIcJe534Xgg+I2gwGg8Hy8kRDQzZBazwR2n9UeyvY8Z0LPI+sAlaRIeP/+1+Hb3/E0OoMjc88ERGRnZEqWEVERGRYiwWGTTWNrK3Ph4AWorW7s6U9s2MH1sEGZNzSClYfHAdCFawB/mBMzBWEe7CCJRFxMNE4Tr4HqwVcDKloLcloY9Gmlvp0K471Ohd5HllfPVhFhoJdvQS1Y8fxsbZxHJFq2NHDERER2WkpYBUREZFhzQE21zSULd/Sm9nuFy7E9SHuG8xgZYZBQNaJFp5aAOOB42JKJpDyB6Ey1AYB6aLzGwvxqIuJx0M9WGPWIR2JM30djMiAaw0mFmPk2CbaYj6tcWiN5ypi1SJAZGiYlR4FJtcPefdsDSNS+t0UERGphloEiIiIyLDmWEMqkrutfZ+W3ARSI6bNpKU9y1497Bsk25mbGkNDEGWNlyFIpXASiR726qMgwMtXps5cA8Z1OfXMCzGOE57kCgiCQZrkKhIPLUpEHIwbIxK0AWCAiK0waZWF3do34dhGAvLvi68WASJDgc1miVsXir6oGdem6nIREZFqKGAVERGRYc21FALWYu9vTnFoD/tmRtZw+tJcdWdAjOx7S4nvO31Ax2dLKlgZMYKIa8B1wpNcAUEwCD0UfZ90pOj8WBJRF+PGcW2Ww1amiARxKsSr7NHyDhFrcayHzYc4z73/DP/gzRn4cYpInwTbthR+bqnN/fOVTW/uoNGIiIjs3NQiQERERIY1xxpS0RoA3myCt0bDorWL2JzysD1MGpUeXR96nnrp2YEZVHNz5yMISIV6oEIi4uZbBIQD1kFpEeCX9GC1EI84mFhuWX16K27Radsj5G45dl3Gbl2LweDQ2YPVCcAbjEpbEekTm07t6CGIiIj83VDAKiIiIsOaA7jFFZj5HzJZn1R2CNzKHvglk0xBPOpgSie5YnACVoKAtFvUIqCxMR+w5paNSG8DwLH5t87AzPUu9bF65tZNxACmaJIr16IerCJDgK1Q8V6x1YeIiIj0SAGriIiIDGvjN6RI5itYDWA6ElZrSWZ7uOV+O1Ri2iAgVRRwGqAm6oLZPj1Yre+TdTu7ShkgVhSwvjGmhaDCFWWNn6HOz2AsODZbWO5YyGS88h1EZPvyfbyIobi/R0TF5SIiIlVRwCoiIiLDmrGGVDRevsIGfb+V3QxC9VcQlN2in2sRYDCEx+cHA18Z6ns+nhtu2x93HXBdfGOpT6+ruN/IbBLIBdZnHPBpIk6EiBOhNdNKJputuI+IbD/WL/8CKRqoglVERKQamuRKREREhrXAqYEKuaS1tu+z3TuD8N11EJBxo6FF8YiTq2Atm+Sq/+VnzXc2F36ef+Z8Ml4+hOkIj088MVfBagwpx+IG6Xwlbfi1j8h29neMlgTB2awqWEV2uAoBq6sWASIiIlVRBauIiIgMX83NGGoqrwsCsoNQEdoXzTMXcv5D/0Iq4nYuPGoOEdeAMeWTXA3CeDOeD9Z2PoxDzM1dQo7dFrCpBuorTJYzMtMO5O4+jhqLH/iFR0YBq8gOZz2PmBvu76wWASIiItVRBauIiIgMa4GbKFSw2vz/t2Zacy0C/N6nDQ4GBiHgdCx4TtElmzFE3VwFqS3rwToYAWv4mK7j4Dq5Kjff5N6fkaltQC01Hbmp9RnhFVWwGostqmLN9tTbVkQGX1D+RUdMFawiIiJVUQWriIiIDGuBU6H/KuQmgOnmlntrK6yrtKyfHGtDk0zhOETzAaclHFQOxiRXGd/PtQfIPzraA0DnhGDjWteW7Tfma+fB/PmY+Y8TdU2hB2vEiaiCVWQIKPRgLfrYqPP056GIiEg19F9QERERGdZMF5dD1vO678FaVi06gOHmwoW5R2trvoK1s0WAyVewVjrn4LQICDrbAwCxSOf7tSKaq1KdtGklxnaGvSMybTSO6Gy9ECsaf+6YqmAV2eEq9GAdoYBVRESkKvovqIiIiAxbFgsmVx1alwHXdlZl2h4qWCu2A7jzzgEbW/NprSwc63PW3a9jbP6SzRgwDpF8BWtQ1oN14CtY03f/T+cTa4kV9YNdV+fgGUutl2K3zS9Tm0kyIrWNI5e/jFs/srBdNBq+5MwqYBXZ4ey3vgWZDAB1mZFYM5WxW+p38KhERER2TurBKiIiIsNa0NX3zT1UsNqgMyR8aFrun7HoGr4+kIMDfDcaXuAYIh0VrCYcqA5GD9ZscfWpMaGANeVa7h6zjn/dNoLG5Ao+8doKABLWwRk5qrBdZ8Vt/pgKWEV2rMZGSBiYk2BzooF3xjZjMRhjWb0lycRRXUz+JyIiIhWpglVERESGNWvC3ze3R3Iz3j+34hmy3U1y1UW/1Yq9WavQ6vr4xpI1JZdrxhB1OypYy3uwDtT5O9w/bjO+A35+GMUBKwfNot0NeD2ylbaIxUDu4QdExu9a2CwaCbcIyHrBgI9TRPrG5nspvzF+Pywdk1s5vLV2644blIiIyE5KAauIiIgMWxYISgPMPGPB664iNOiiCrOr5VXySipYjeMUKkJtSYsAay0D3SXAIXz+WDxavpG1/C2xBeMHmKxH7erNmEhncB0vCVht4HcfXovI4HMMFlg9YmJo8ZLVW3bMeERERHZiahEgIiIiw5o1bsXlxoLXXQjYRZJpPQ/jDtwlVuCEj+W6Do7pqDYrn2grVxlqGAjW98HEQsvi8fDzfVsMtVnLikiS+PIkAJEgPOZItCRgtRYvCIjpu36RHccYNtbuSfFvtMVW7i8tIiIi3VLAKiIiIsOWxXZdwQo992B1DLk62LwJEyrOzF2N2x6ynHcyREou1yJO53gvm/sj7l9TNH4LvrUDdoFnM2l8EyWZP2B94BJPxLvcvhDrlrwHpS0CCAJVsIrsYNbA5trdGddeslwBq4iISJ+pbEBERESGLfv6a91XsHZ3v31JCGGBNa1rsL43gCMsbxFQPGGU45ZUqlo7oMVnNpPBL+lRGyuqYJ1/5nzqfRe36G2yAG74PY24EbBFA7NB1+F1c3PnQ0QGjTWGtlhT+YrAJ1CPZBERkT7Z7hWs7777LpdccgkNDQ3ceuutZeuTySQPPvggzz33HC0tLTQ2NnLsscfyyU9+kmi0Qs+vflqzZg2/+c1vWLx4Me3t7ey6666ccsopHH300d3ut2zZMn7729/yxhtvkMlkmDx5MqeddhqzZs0a8DGKiIjIIDEGSzgMrMkCxuJ4rd1WsBIEldsEeAMbsD64f5Rp+Qoz10Ik0hmwRowDixZ2brzHZPwBDEZsJoXvdF5/GWtJxMLXY/N/Ww9btlDrWZaOhsAxzG6vD21jIi4jY7UEHWFtEHQfXovI4DMGx3pAuO0HQYDnW2KRgWk1IiIiMhxs14A1lUpx0003kc1mK65va2vjsssuY/ny5QAYY1i/fj0PPPAAb731Fj/4wQ9wnIErun333Xe57LLLSCaThfO999573HLLLaxdu5ZPf/rTFfdbuHAhP/7xj/Hzt78ZY3jzzTe59tprOf/885k7d+6AjVFEREQGzz+fmKTJ7aKCle57sHZ1G+1AV7A6JRW2saLxVqxgHdCANdMZigLYcAVtqb03dvRwbA0tN26ED+1yEO1BfnKuwHYfXovIoLPGhH+/O5YHvnoki4iI9NF2DVj/8z//k9WrV3e5/uabb2b58uXE43HOPfdc5syZw5YtW7jttttYvHgxf/rTn/j4xz8+IGNpa2vj2muvJZlMMn78eL7yla8wbdo0li1bxo033shvfvMbZs6cyd577x3ab82aNVx//fX4vs9ee+3F+eefz+67784rr7zCTTfdxO23387+++/PuHHjBmScIiIiMngmeDEyXfRgBch2F5baLgLCAe5f6JZUl0VinZdvTunYrSUYoMrQG65biL3zn9h08L5MzJ/GWEs8UnLOWbPg6adJRrp5r9wIjy97hKybq2w9pukTXfZgbZ65sPDz/H6MX0S65xuDNU6ojTSgHskiIiJV2G5fSz7++OM8+eSTGFP5VpMFCxawcOFCgEIVaCQSoampiYsuuoi6ujruv/9+WltbK+7fV7/73e/YvHkz8XicSy65hH333RfHcZg6dSpf/epXsdZyxx13lO137733kk6naWxs5JJLLmHSpEk4jsPMmTM5++yzSafT3HPPPQMyRhERERlcNdYloHIFK0A2282EVfkgdUIrxHwwHXfZDNAkVx2MSeAE4ASAtSSKeqA6jsEQboA6kLnI6x+8gu+EA97aWMn7NX8+1NdDF9d4ACYSyd+KnBcEeJpIR2SHyka6aL+mFh4iIiJ9tl0C1lWrVnH77bdjjOHkk0+uuM3DDz8MwOTJkznyyCND62pra/nwhz9MJpPh5Zdf7vd4fN9n3rx5ABx//PGMHz8+tH6//fZj6tSpLF26lA0bNhSWb926leeffx6AU089lfr6cH+xo446isbGRl566aUu2yCIiIjI0OFgCIxLewSCCldFXk89WCuwA3SLvnFdamvqMCZB4OTHZww1iXjnRo6DU3Q+a4PqWgQ0NhYeC5c+zcKlT9OaaSUdrwmPKbDURCsE0rNmFSa2MlAeMrsuxhYtC2yuQk4TWonsEBbIRGPMXAP1mfzvbce6oJtJ6ERERKSiQQ9Ys9ksN954I+l0mlNOOYWDDz644nZLliwB4Igjjqi4/qCDDgIoVLn2x4oVK2hra+v2fB2TVRWfb8mSJQT5P6Yq7ee6LjNmzCCdTvPGG2/0e5wiIiIyuGqsS+A44XShiOd1PZt2Vz1YCfpfwZr1M9SNGceR20biO/HQuuKA1RgHp6RVgd+fyrMtW3KTdHke+D6pWF1otRv45S0CSlgohK2FcboRHDorWK0Nug+vRWRQ+TG3mwpWX7+fIiIifTToPVjvvPNOli9fzj777MMZZ5xRCFKLbd26tRB4Tps2reJxJk2aBMAHH3zQ7zF19IGNRCJMmTKl2/OtWrWqsGzNmjUAjBs3jsbGxh7HOWPGjD6Nq6WlpcdtGhoacLuYjENERER6z/o+8cDBmq7/u5oLAruYTTsIwDEQQMqtYVuiCc9shQG4tTbVVF+4494rDVhrE51PHINrAwr3zQzwJFeZaCL0vC6d7LLdU7dct6xFQLaL9+mG+7d0PrmJcHXrfHVlFRkQxpB1uvhT0FeLABERkb4a1ID1hRde4OGHH2bEiBF84xvf6DIY3LZtW+HnXXbZpeI29fX1uK7LunXr+j2ujvONGzeuyzGNGjUKIHS+jv0mTpzY5bFHjhxZtl9vnX/++T1uc9ttt9HU1NTnY4uIiEiY9XKxpK3Qg3VaC2A9+PWv8eZcWnk27Xzl6Nq6JpaOO4JsJEY6btmczDC2n2MLohE6Zp7x3KKQ01pqaopbBLg4oR6s/Z/kys+/1MBAtq4htK42nazuoKUtAmz3tyBPbcm/hny4Wpj46s5m5p+pkFWkv6wxeE7lv4Os1SRXIiIifTVoLQI2bNjAbbfdhjGGr3zlK92GgsX9Smtra7vcrra2lra2tn73N/W8XAVFXV1dl9t0rNu0aVPZOLvbr6Mva/F+IiIiMgTl+4QG3VSwAl0GDR0tAhbsuj++E80fK8KbmzL9GlZxD9czXoHA6bw2MtYyItH5/bgxBmfLFmhtzT2w/Z7kyrUwynOJ19SRLKlg7SlgXToa3hpjcpNeFTFupHIP1gpMTYL0Lk1kxozClk1vLiIDwkBgcn8KBg7h3zTf1yR0IiIifTQoFay+73PzzTfT1tbGqaee2mXf1Q5OftZdYwyxWKzL7SKR3HAzmQzRaBc9g3qh43y9PVd/9+ut2267rcdtGhoa+nxcERERqSAfsFrT/ffNXVZa5vdfWz8mtHh1az8D1lQuxAww/G23Q7HF34dbqIsVXb45JT1Yq2wR0HxaKwA3/tnwuQ1jaPAj1NRBWyz8xXd9sq3yAebP5+0fNxI4Wyqvd92yHqwXz/shbfs8nlttDS8C/uaNxEc1ECThnkNjvDtqKVfdsw4qd3QSkSrZfffFb8191jilH3HdfAEiIiIilQ1KwHrffffx5ptvss8++3D66af3uH08nrvVrSPA7ErH+nQ63W0VaU86AtLuepl29BcrDko7xtnX/XpLt/6LiIhsPzYIsJhQwDqlxfJOk8HYzoquLgNWa/EqVL+2JPs3yVUuYLWsaJjImpG7MXVjZwBirKU2VnROY0oC1v5NchWpraXBz10ebouPoKVu99D60RPHVXVcE4mU9WCt1Jrhm/9+KucD90yHtXVQFxis70P/5w0TkRKBmy9yKVluA5+sKlhFRET6ZMAD1ldeeYU//OEPPfZdLdZxW73v+2zdurXQx7RUa2vrgIyxN7fxd0y6VXybXrX7iYiIyBDke+H2ALY8aADIdNkiwCcTiZKbjcqSTOSOFfQzmLDpFGDYXFNhQk1rcYonmTIO7gBUsHZwa2rIOpCONPDY1OMYUfR9cSbZzqhM1y0CZk2YhTtyYe7JtFmhdWUtAqwFW36N2NSWey2pyEhWjZqFH6lhwZTFWPtyxX83IlIda8Dvqj1KkJvcT0RERHpvwAPWJ598Emst27Zt63bSpvXr1/PZz34WgAsuuICamhqSySTr1q2rGLCmUinS6TQAiUSibH1fjBmTu5Wvu4moNm/eXHauavcTERGRoccGfkn/VYvFEgnSQCK/pJsK1iAg7Za3LOr5q+UexpXJXe+0R8v70pvSzMNxcEKBqqXaAtZW1wfHwQDbEnsxLtW5btE4y4L6gEt+8EB1B3ddXFuU1gYB2PB7Z63lsw9/wEu7zSaI7AEGXB8W7n0wE9w2/My7bNAX2CIDZl2tg+/kKuT3aYEaj9wXRs88jTdn9o4enoiIyE5lwCe5cl2XaDTa5aO4orVjmeM47LXXXgAsW7as4nHfeustAGpqarqdCKs3dt99d2KxGOl0mg8++KDiNkuXLgXCt+1PmZJrALZ69WqSycoVHB3j1O3+IiIiQ5zvE6Eo5LO5R9RPhTbLdBOwZiLlfdl9G/SrijRXwQrJaE3Zuukr3wg9NyUtAqy1VbUIaHV9fGMhsIxvhVR0LPX5PLQ+A8bzmThuFPFIdfGxcSM4tnOSUmsDat5fz5FtDRzV1sBIP4Lfsp718dE8ttceWCAZzT382jijao/iiPQYRry+tKrzi0gpW5jkqmyNQRWsIiIifTTgFaznnXce5513XpfrX3vtNS6//HLGjh3LrbfeWlje0tLCa6+9xrPPPssJJ5xQtt8rr7wCwOTJk/s9xmg0yn777ceiRYt45plnCpW0PZ1vzJgxTJw4kdWrV/P8888zd+7c0D5BEPDaa68BFAJjERERGaKCgMBEmLoRarLgBhZTqGDtlPW6q2CtMPGltXi+JRap4qb25mYCxwMsyZIKVoNlZknAOlCTXHUez+AbF9/JnTsZgcDAtkhAJP529cd1Xdwgl9geuNqHjQvYI1HHpGwuRB4dxPDXrWZ93Rimr8udtyafxxog69bwxoSPMa3teYL2Npza6nvxi0ju+6SOgDXoyFmL2o9k+9HLWUREZDga8ArWah199NE4jsPrr7/Oyy+/HFq3detW5s2bB8DBBx88IOc79thjAfjzn//Mxo0bQ+teffXVQgVr6fk69nvggQdIpcIVLo8//jibNm3CcRxmzpw5IOMUERGRwWFLe7ACru9jiidjArwuggZrA9IVKlixtl8TxOQLaUlF6qBosq0T33yCuFcyiaYx4R6sQFBlMGIsjEk5eG4DdVkT6nka2/JnjNNe1XGho4I1PPYpGx1c6xINYIQfIfPOEjbXNJTt2zHBl+/EWVt/KN7mjWXbiEjfddmDlW5ao4iIiEhFQyZgHTNmDEceeSQAN910E0899RSZTIZly5Zx9dVX09raSm1tLc3NzaH91q9fz+c//3k+//nP8+STT/b6fIcffjjjx4+nra2NK664giVLlpDJZHjhhRe44YYbANh3332ZOnVqaL/jjz+euro61q1bx5VXXsny5ctJpVI89thj3H777QAcddRRNDZWmJhCREREhg7fJyA8yVUkyIYrQuk6YCUIyJpIbsKmYtaS7cfttdZYPhg1MXwqB2JeO65TcvORcXCyGfD93MNa/CorWOPWwRpIxkYD0B7Nhbvx7Gba2VDVMQtKe7DmzV41mrX1e9IaHU32W98k44wqBKoArSX5dcYdwZr1m/s3FhHJVbA6XbQIoJvPPREREalowFsE9Mc555zDihUrWL58Obfccgu33HJLYZ0xhi996UuMGDEitI+1lmw2dw9ZX2btjUQifOMb3+Cqq65i1apVXHrppaH1dXV1FVsdjBw5kq997Wtcf/31LF26lO985zuh9WPHjuULX/hCr8chIiIiO4b1fQJTfClkcX0vPNs9dH3LfeB3WcHqVVn91TxzIUdsrWfO+weElo9It1K3ZTORfWeElhvHwQlKWwRUdWoSNhe2pCOjCsveaoKRqU34R3yI+WfOr+7AAK6b68Gafy8X1beyVzs8PfloVuVP9+CB7aSi8bJdfaczaPUDj7fWbGEP3Sgk0m9d9WAFyGa9LteJiIhIue0esB5wwAHcf//9FdfV19dz5ZVXcu+99zJv3jwymVylw8SJEznzzDMrtgcYN25cl8fryZQpU7j22mu54447WLBgATZ/0X/AAQfwpS99iV122aXifgcffDBXXnkld9xxB0uWLAFyAfChhx7Kueeey6hRoyruJyIiIkOI7xMYFwdoi4FxYFQqwNiAN/NzVW5ItDC9qxYBgSXt5ibJmroR/C25YNZaW/3tta2txFIxNtY28M5o2HMz2FSK2Jo3WO5vZuz8x8PbG4NTHABXOckV5CpYAdKR+sKyaRvho9tGcvBZ/6/H/eefOR/uaK64zkQiGMC1WcDlwFUeyfxVaDI/z1hbrLawzGLJkGaf9a+QjB7aeRwMb7ckmZP1SUSrm3BLRHKV8qUtApyOz47WVgWsIiIifTSkKlgBEokEZ599NqeffjorV66kpqaG3XbbbdDON2HCBP71X/+VzZs3s27dOpqammhqaupxv7322osrrriCDRs2sHHjRsaPH69gVUREZCdywUP/wgQ3Srwjj7QwMWVZHA8obkDa5S33gY9fess+5G/Tr35crnXYWNtQdDhL0+YPSCYqBIplk1x1U3Hbg6h12JIYQXtsdGHir8jWNsbZdM8798TJjT3XhzU3sVV9JleZOn0dvD06vLn1fT7x4mqW77aC495tYdH4EwvrvHSGda1p9mgMTwImIn3TXQWr5/ldrhMREZFyQy5g7VBTU8Pee++93c7X0NBAQ0NDn/cbM2YMY8aMGfgBiYiIyKByA4vnxIkXcgRLIpvG0BlYGui6ItRa/Eo9DPtRRQqwclQ9dVsjhcmtsNCwrYXauvryjR0HN/CL+sBW3yIgag1P7HVE4fk218fGLQ33VHenUDFjDJ6x+T6sNV1uV5fJvedbjGXcxjX8cp/1nNTeQMRvIR1pAizWy7JBAatIv1X8/MrLqIJVRESkT4bMJFciIiIi25MbgOd09vy0FhLZNE5RD1YHug4sfR/fdcGY8HJrq64i9a2P74bLOWvSrcS9VMXtjXEwReey/Ti3ax22xctD3LrYwNyK7zngBunO25DJVbF2CJzcA2MgEmFs2ya2uB7pLZuJeVuoz+a2X/Dm42xpL58wS0R6zwKB6fp32/MUsIqIiPTFkK1gFRERERlMkQA8Nw65uTIxBmozyVAFq5vx8buYRNMGQVkPw9yKbqpee8E6daHnI9o3db2xY3h0ss+GfC56sLUEVZ7bdepIRmDGWmiLggks9a0bMKUBcpV8Y3G7aDcwrQWcjrfZwLiaJiankhhyk5hG/DaybmE1W1uTAzImkeGsuEVAfdYUVcKD7/l4QUCkmypXERER6aSAVURERIYl10K2qIIVCzXpFMZ2Xh5FMXQ5X5UNurjFth8tAqzFEA0timUrV68CYJxQIAy2656x3XFdHDsCshA4BrBYCzOWPdu348yf3+Uqz+QqWLsS5N9Kd+Qo9tm0hrpMil/+yYDr4Nj2wnZ+4NPe3s17IiI9spS0OCn+3PB9bBDg+ZaI8lUREZFeUcAqIiIiw1IksCSLA1YstekUhs7enhFrupnkqqsK1ipDTiASGDC5MU1fB8kIxLLdTDJlHEzZJFdVnRqHWOi5ay2NqW3VHawC31jcoHIwGgk8jHWwxmHa1tUcve5dAA5a70J9PVmK9rOWTHoAJt4SGea6m+SKICDjBySiA9MiRERE5O+dvpMUERGRYcntaBGQZy3UpNoxRT1YXUz3LQLGjIFRo7BFd9Hbfkxy5WLwTbiCNep1EyY6piRg7UeLABMOWEckN1d1nK5MHLUbE2pGYMi9WcWjPGDNaxy77M8c+86fOfrLn8El/Bp8mw09z6TVg1WkP6wBv9uA1SfrV9/qREREZLhRwCoiIiLDkgkcglCYaanfc0+MDXCDXAAbDQxeV2FeEBBUupTqT8hpDb4Toy0G7fmhRbupYDWlLQKqrZ61MMEbUXj6TpPhiaPGcdFVc/p+rC74jmHt1vfwTW58gZMLWR0L9eltJLJparwUbuOY3A6zZkF9PcyaRcqE34NsJlv1ZF4ikhOa5Kq417LrYoOAbJf9UURERKSUWgSIiIjIsOQEJbe+WogHXklPU/Cz4erJgiDA/9DhuaTwuSdwOkJVa/GqDP9G2ghBSSVppNsKVgdnACpYp2Rq8E3nZeHM9S6TT/pHTjn+sD4fqyu+odAiYOF4iGBg1CgmvbuJuszm/CRWluhukzp3mjUL5s/n1icf47z/fbDoYB4ZT7cvi/RHaYuAhRM6fvKYnW8RICIiIr2jgFVERESGn+ZmzF5rYMQBuPkMoTEbIR4E+E7J7emeV/EQ1gb4lVdUFXIGrduYk2zir05xwGq58ugUeDDiyFmUTSHlOPjGEnQUn1VZwTo7OZLV9VFqPHCsBXxi0WiP+/WF70AkSFKXyU0wlu8UwLxDxvCRv20kE4mSTbbjNo0tmywrlogTcTovW63nkVV/SJGqWQhNcmVKPzdUwSoiItInahEgIiIiw5Ix4e+ZjbW4NiAgHKj6XsUYNVfBmm+++tbozgdV9mDNvr8MCxy4PkpdBmqzYAMLtut+o8YJT3JlbdDnSa6stWwcFWNjXYxtMWiricCoUcRjA/s9fGDAYElGfHyH3G3IwHlHnkt7+za2bd1IKlt5EqxYIh56bn31hxTpHxtuEZD/4GiN5R6PvP0X/Y6JiIj0gQJWERERGZYcSieT8jCAV1KX6vvdBKyVlldZRRq0txIYl5qsgxuAE+Qm0sJ21yLAxZRMCNXn6tn86zv6PZf6DCRSuYA5Fhv4CtbABuyxeXnhvL4DR+41BlP0v0qcRE1o8jF8r7pesyIC5CpYi1sEGGtDVayOhYynClYREZHeUosAERERGabCAWLMz/Va9U3vAlbrB/gVA0FLVXfWeh6+Ewt9+x142R4C1nAFa66qtm/Bo81mmFA/Ac/JvR8GoLV14ANWY3CAaevfIBWpwYmOYZ/DD2bS6Fre7WFfE4tjbIDNV9xZ36+qSlhEclXrmFzA2tERxViLG2SBXLW4YyEbKGAVERHpLVWwioiIyPCzcCFkwwFdJB+wjsuE+3r6XaSlfuDTkWVG8rWXhlx4UU34Z7MZfKcz1DTWYrNZsF1MskWuRYBjbG4GcGNy/V/7GrBmMpw560yI1oTi4nh9XV9fQreC/FVnwkvzoRV/5dNvPMKs0bnv+l3HLTwqMZEIprheOAjwFLCKVCfI/S4FjiEZgfYo8OnP4Ba1IzFAVhWsIiIivaYKVhERERl2LGBMSQVrfjKrlJ8MLff9AGstxoSrVf3AwqJFuZ9rLPl2rLyw8jn2t4f1fUznnot3aCPRfG7oJjPEuum/CuQqWCkKQWzfq2dtNlchmw3o/Ord94nVDmzA6plcEE1RSwOnfiQA9bH6bvc1biRcqVtliC0iFNqC1AcxfONAfT0mEsENOj9vchWs+h0TERHpLQWsIiIiMvwY8NzwxEmJTBpcCGy4JYD1fQILbkk3gOCHl8KBH8k9qQkduroKVsdwytIY86Z2LLBE/V4ErMXBI32vYCWbq5CN+hG8fMDqeh7x6MDe6BRUOJw7sgGAi/51VmHZ/Eo7u25ZBasCVpHq2HzAGhR/aRSJYIqq5Z3AqoJVRESkDxSwioiIyLBjjSHrhAPW2kwSasDHK9k4ILAWt6Tfqud2EUBaqpqAyRpIR2JFCywxL4Nxu75cM47LlhEuyUS+N6m1fZ7kymbyFaxuZ0VvpD1NrKvXVyXfGByTq2DtmMzKyQesPXLckgpWBawiVQsCAgy26DPNuBFcm8HPL0qlt5Kpqpm0iIjI8KQerCIiIjLsWMew/7pcmPn2aHhnNNTc898ABCWTXBFUvh3dN5WDz2orWDGGjFvcgxWiXjcTXEF5Bau1fZ/kKpPBWsjmg1zH8zGBHfCAtTVWPiFYbwNW47oYAvzAxw98Fq5egKcJeESqYn0Pa0yuVYDvQ2sruEUtAkyuyvWqJ6/dsQMVERHZiShgFRERkWHHOoaMG2f6Opi+DmZucKmJ5wJXa8PBnbVBxdDS7yKANLbKFgHGkI4WV9VaooHX5fYAOA6U9WDtW/Bos2n8n/4Ma5zCMYjFiA5wwHrPeY9io1GcfNWc8QOcESN7t3MkQmO8PjQRluf18N6ISGW+j19SkW9cFzcoahGAQTc7ioiI9J4CVhERERl+ksnQLfHGQiKSuyyytrRFQFcVrF3MeE91LQIwhCpYsRDrTQUr4XMFfh8rWLPZQvVqbkFu/4GuYDXRKI+NaSMIfHzfo2ZjG8bp3TmMm2sR0FHB2pppxff8nncUkTLW93MVrHnGWnBc3KDz88a1YG3lzzgREREpp68lRUREZNixjsFzwqFiR8Wmb0oqQLuYUMlzy8OHaS1gNrfi33Aj3Pmzvo3JGNKRGMn8sLx6Q2PS5Zfz6wE46Bfl0z+Z0t6k5CpuA2vz/U57cd5MuhDsBg65hPh73yMa6d3+ffFqZBOTXssF2FG/8/2ff2bFqa06OREM4dfpKWAVqU4QEHz+n+Avr+eeWwuOE65gtQajgFVERKTXFLCKiIjIsGMdh6wbK9zKYyzE8hWsuelfAiydt8yXBqzWWvxuqi8D07fqTxsE+QrW4kmuIOb3poI14MDV+bBx4yLYfyZBYHHcXgas2SzZkrDZdQyRXlaXVs3vfUBqXBenJGBVBatIlXyPoLRFgOMQ6ejB2rEsUMAqIiLSWwpYRUREZNixjiHrRol3ZHS285Z431iiNgj1JC275T8IykLUaS1QmwU8H6/ChE7dyoeN6Ug0tPjAhr3Yx63rej/jlFWwdoy31xd5vk/GDW890O0B+s11cWw4UFUPVpHqhT41LLkKVlsSsFq3T9XwIiIiw9kQu3oWERERGXxZ1w0FpMbaogpWGwotbRAQlLYIqBCwFvP7WsHaEbAWVbBaLPEeJrkyrlN26zzW0pd5rqzvkXWiJCOQjMDW+uiAT3BVUYUWC10xjgMlrRt8v2+TeYlIjg2C0k+NfA/WcMDqWMjq90xERKRXFLCKiIjIsJMpqRQFiOVvqQ8M4dCy0iRXtoeAta+31/u5ILV0kquEn+1ihzzHoaQWLde+oC+TbPleySRXQ7CCFSh9nQpYRapkAwJb2iLAQFEFq8XiWEu2j5PmiYiIDFdD8epZREREZFBl4onwgoaGQgWrjw3fjm4DSjMG20MFq9fnClYP3xiysfy4jIFeVLDiuLkZwEMHswR9CFit54WDXTqreQdafawex+Yq4/qqbJKrPvRwFZEigS39WgZjDAHhns+OhYynLzJERER6QwGriIiIDCvNdzazJR4LLYsEQWFSJ9+EWwQQVKhgDQL8bvoSBsbpU8iZ64NaXlUb76GC1RiDNaUtAgJKh9v9uT2yToR3mgzvNBmenTVmUCtY3xpjeGuMgfr6Pu5ZUsHapxcpIgU2oPS3xzUGz1gc2/mlTkvbejKqFBcREekVTXIlIiIiw4oJLKkRuYmjkhGwBho+fnJhfQDhHqw2KA9LgwDf6b6HqB9YHLd3k8NY3yPjFoe+ufPFeqpgBcq6KVpb3jO223P7oRYBgWH79GDtK2Nx8+95faweXxWsIlXJtRHp/GwyNlc0/36DwZCl409E1xr1YBUREeklBawiIiIyrCSXvUHG3T20LJaIF372je2xB6sN/G5bBEAuYI32dh4n3yddXMHquERch0gvqmCtgXcafKxxiDitHNrHFgF4Htmic1sDscjQmzX80/t/guc2d77nnipYRapjbe4rnKIq8o4K1iNWeCycmFvmgCpYRUREemkIlieIiIiIDJ4DtsXJuDF8B3wnV7EZr60trC/uwWogV61aqc9pFwGrE1jsurV9CgCt75dUsEK0pwmu8gITrrilj5Nc2cAn6xRVsDJ4FayzJszCddxCJWpfuCUTh6mCVaRKQUBA0ZcoNtduJGsskaDzc8exRpNciYiI9JIqWEVERGTYSUfCYWbtiBGFn3MtAopCBRuU9/v0fYIuerAaC2SzeKd8HB7+394NqKgHa33WYGpHMDGIwgN39bxrR89YmztOX1sEUDLJlTUQH8wWAR1Vc1Nn9Wk3Nz/xVwdfwY9IdWzpJFfgOpB1LJGitiSORS0CREREekkVrCIiIjKsuNaQLqoW9T2PRCIfMM6fzzu71WBsUXVkpcCymwrWDl4P60OH8z3SkfAkVz1NcNUhgFBLg1x/xV6fGhsEZN0IM9e7zFzv8pkDPks0MvQuEZ2S0DcbKPgRqUrZRHgGYwx7jd0nFLC6qEWAiIhIbw29q2cRERGRQRQPDKloovDc97LUFDVL9U1JYBlYPFveg9XvqQer04fLLN8Lhb7GQrwXE1xB5RYBferBagMyRS0CMIbYEJzkKlIyYVig4EekKjYIsIR/n1xj8B2I+MUVrIasp98zERGR3hh6V88iIiIig2iXjVnSkc5JrbCWRChg7ezBmltfoUVAEPRQwWrwTO/7jNqiFgEd+8f93gWs153wE6LWEukITPrQIsAGAVgbmuQKYwatB2t/uCXvd1/6zIpIEQulHYyNAc8xoR6sroWMWnGIiIj0inqwioiIyLBhfR8nCPdgtRCqYPVMSQ9WIPBK4oieAlYD2T4ErHjhClaARNC7FgHGcXK3z7v58/Vlkqv8bfYZt7SCtXJ/2R3JLRmTrwpWkerYgNJPCNcYPCc8uZ5rjVoEiIiI9JICVhERERk27PEfxoy3pIoqWK0NSEQ7w1LfWAwBrfm885kVzzDbnxM+ThAQ9NACIHTbfU/jCnwyHT1YXReOOYbYsYf2bmfHhZJJuXpd3Jnf1isaqzGGWKQP4XAfXfSvswCYf+b8Pu0XKQm0PVXWiVTHBgQ2/IWFMZB1IeZnSOY/Dqzvk876NP64sbDdpu9t2p4jFRER2WkoYBUREZFhw+brtoorWLGWRFGgWNbTlArVkjYgyPp0lWRaAxmnDyGl74d7sDoO8d5ONGVMqGcs1pa3NOiC9QOymHA/xkGsYO1rqFrMjYTH5AUB1lqMGXrVtiJDWmAprUt1HUPGNUT9TGFZxEJaPVhFRER6Zeg12BIREREZJNaAbyL4HRWb+YC0uEXAqu+sxSltEVAasAYBflGwl4wZ/FDOZ/pWwep5Zbfp9zZgNY4bCoRtXya5CnyyJZVsQ7YHq1sSWPelFYKIFFgbhAJWQ65yPetArLhFAIaUAlYREZFeUQWriIiIDBsWSEdGEs+3VLXWgrXUxYrCO9etUMEa7sHaXYuAhRPgvVjAlbVLWNXbgZVUsOI4xHt7m77r4JRUsPaygDU3wVWFgDXW2+rZ7SjqlFaq5ip1h+BQRYa2wIar1gHXkKtgDYorWA3p0v7TA6G5ufPn+dVXtYuIiAwlClhFRERk2LBY0pERoSm061JbiRRXbBrDR5ZZ7h6Re9oWAT8IaL6zmYVrFgJwSmI6nzRjKxw/fwgMpmZE78flZcm40aIxOCR6mxyWVLD2qUVA4JMpCVoiERdnCN5275a2XAh6/zpFpJiltIWxYwy/PuO3/PXezn7TEWvIeAG5mx4rV7I239kZlvanBYiIiMjOTt/5i4iIyLBhDfhOZ6WotZaadFtoG2MMTkkFa2mLAMdCUDLpUiTorAkzxmDofQ/WjOeHjmecvrQIcMp7sPa6RUBQVsHa68rZ7cwt7QvbhyBZRIoEAfaee2DLltyDXMBqojGmjJhQ2CxiXAh8DNGujiQiIiJ5qmAVERGRYcNiCZx40QJLpGhSlw5uEL4ttrSnqWPBN51B5FFbRuGm1+M5uSDCYDA9XWYV3SabvuRfobgNgelDiwDjEPhp/KDQ9wCvtGdsVyoErLEhG7CWBs4KWEWqYm3ZF0SOYzDxOPHAwxiDj4XA556F/40hhiU9oEM4dL+nAXj7x41s+t6mAT22iIjIjqAKVhERERk+3nkHz3RWY1lrQ7Nmd3BLgruOIG/q8lamLm/FvPlWWQ/WV8dlaYuCb8hXsvY+qEx7Pr6xhYfjOkRLKza74jhVtwgoD1ht78+7nbmuS3EbVmstngJWkT6zQUBQ0gbEAYjGcIMAYz0suS+kHGtxiFU6TL/UpH1q0j5Tl7cO+LFFRER2BAWsIiIiMmxYxxA4xbe7WiJeecBa2iLAD8pbBPglFWBu0Dn7toGeK1iLpLPhitmY62B62QfVOA6O7dy/L8GjDXyyRT1Yjc2de0gyDi5Fr6svrRBEpJMNKlewGoMBnKKJrja1rccMQsBKfoJB/EGYREtERGQHGKJX0CIiIiIDz2Yy+E6U9nz2abtoEeCUtgjIB5a+9fGtTyqbKgsoXBs+Tl96sKa9cIAbj/bhNn3XxeQD1r1bLDz6CP4PLu7dvtby0DP/Tavj0ep4AMR6O7nW9uY44XdUPVhFqmNtWQVrx1ODwZLpKMMn7hsmrUwzddkWpi7bsp0HKiIisvNQD1YREREZNqxrCEzu8ueVcfB+HF7fP+Csku3KJrkqrWCFcEAxcybuotcLT4019NQioKMHIcD/5309tK5PE00ZE6pgBfCcXu7v+/gmfDk4VCtYjePgGpubqQwUsIpUy1qC0U0wclTu+YePx81/nrnWhCpYawKHNhOvdBQAFq5ZOJgjFRER2WkMzStoERERkUFgHYdtsQhB/grIN1ATLb/91Zk4kcAxBPmmn6VBnmNNeQWrawtlYH1tEZApq2Dtw3fgjoshHLCWti/oig0CMk7nubzAIzJEA9ZcBWtJiwAFrCJ9FwRYiitYTaEliXP9TTi2s92J4/ngdB2wioiISM4QvYIWERERGXjWMVjTWd0ZGLB4Zdu5Jb09g8Byw3ULqUtb6tIWJwjKJrlygyw1WYtrIeoHfWsRUNKHMNaHFgGlPVgBvF4GrPg+QdH7YSxDdpIrHCc0yRWa5EqkKtba0BdExkDHr72JJ3DpDFgj1oAZhB6sIiIif2fUIkBERESGDes6hYC1xoNGL0L9mGll27mlk1zddRe0tmIAS+4b6rJJroqqvnKdDHsISfOh6oIJll++fCezmVyoKUv0pQer42JKxus5va1g9UOBM4DrDN2AVRWsIgPABvjFLU6Mwcn/3ptEAhOkC6siAdBNi4Cq+rI+/TTOpPzPbh8+60RERIYwVbCKiIjIsGCDAOvkerC+Og7aYgZ/3Fgw5bNYO5T0YC2dEKZSi4CgsxI2t3UE29Us983NnbNoQ9ks3fFYH74Dd8MVrE80bOYnu6+i+c7mnvf1PYqnjooYl0gvw9ntLdeDtfO5tRa/q/dXRLoW2JLPL1PowerEEziBKlhFRET6ShWsIiIiMizYTG7ilrpsBGugtdbFdwymJEwFcEpbBJSEqU6FZa7tnBjGAMYYAgsV77hfuJCayR0DgznLo6SKMoy+9GA1xsEtCkRy80D1MhAJglCLAIDIUK1gNapgFRkYNtyD1RTaR2PiiVAP1lzA+vfRg7X4S6f5Z87fgSMREZG/R0OzREFERERkoPkeAQZbFIxaA6ZSBWtpi4CSClaHShWsRSFn/p9eUB7eFmuP5C7Gsm5RIGoM8Vi02/3Cg3FC4W7uGL3b35b0YGXkqKEbsDpOuOmCAlaR6gRB+PPLdFawmkQCp+TLIncQAlZjwdGvr4iI/B1RBauIiIgMC9b38JxwtWYAuJQHrJGSClY//530BC9OxDq4OGVtA4p7sBqbW+cFlorRRGtr0bkMWTca+tY7Ee9bwOqEwl0DJkqv7p73d54erLkWAeEKVk1yJdJ31tqSL42KerDGwwErgGsSFT4lq5epT3Byez0GeCHSNoBHFhER2XEUsIqIiMjw4Hl4bueljyVXwYqp1CKgvAfr7u4ojkrmKk3TFSZmccc0wsrcz4UKVr/nADBiDRk3RqIowYj1oQercVzcIBeI1GbBeB7WZKGo3rPxx42Fnzd9b1PhZ+t7BMblnabciA8+9NghXsFa/H6qglWkKkFQ0iLA4HRUsLoRMOGANUKcTGGKv7B63+Wlcbn+040/bgx9vlRifZ/UmBGFP0IPSo4gSKdw4omqX46IiMhQoIBVREREhgXre2SdzkufoFAN6ZVtW9qD1XfAr+m8jd8aJ5w1GAcHn45DdrYI6DkAdK1D1o0S9/P9DkeOItGXFgGuG2oRUKiEtfljNDfDUa341ieZKAmGfZ+D18dIRtzC6xi6Aasb7merClaR6tiA4Kg54Oc/D42h+Nc+TbLws8Hg4uT6sNpUv08dtG7Dms6w1sXgr/kAZ9KUfh9bRERkR1IPVhERERkefJ9sSQWrMRZjykO6skmuMKGKL1vSf9W4LgZLJuLjOxA4HS0Cuu/BCjAqE8MAbTHAdWHmzD71YDWRKI71C5N1ORawFms7X6tv/dxtwYEfmujF+j5+APh+7uEYIu4QvTw0hkhRqm3Vg1WkOoEtu+XfKWoZkDZJinuMuBhwBqbCNNi6uXxZ27YBObaIiMiOpApWERERGRZKK1h9A5WqV6E8YGXrlvykMLlYwpZ+R+26ubYCgU8+jgAg202LACeAKZlajtm8a2i5iUT71IPVRKO5iWiCLBDLVdFaS+llXjKamzt84ZqFnQsDH7+4L60xQ7gHq6tJrkQGgMUSdNEiACDt+jg2Q5Cf3CpiDZgaYHP/z51qB2BNff65sdhstps9BtBTT3f+fOb2OaWIiAwfQ7REQURERGSA+T5ZtzO4DLBgKk/d4hYFrDM+8LC+T+B0XjaVVbA6LoaAiM0dr3OSq64rWGPWMDPVwLoRH8r1gwVaXZ9EIta32/QjETYkLK2RNJZ8JZoxWNv5WqdtsNRkoT4d3tXPZvEj0VzlrOtinKHcIsCUTXLl92omLxEJCYJwhxMMRR9vJJ0AN+j8sBjIClbrhb/Uyrhsv4BVRERkEKmCVURERIYF63l4ToSZa3MB4vNjRrKRyj0FHcLBqHVMvoI1/7xCBavB4ga58CCSrw7rbpKr3b0aWkYcQCYyKrR8wsgExvQ+5DT5tgeOzYUU7VHD2jpL24Z38mO1HJCuZ+9NMd6JJ1k6snNf3ysJmB1nyFaw4rjhSa6sxe9FCwYRKWEtQThhxS36zEk6loYgRZaRuI5LxDPg1AzMqb0KYWo2U75sELS6Pn6FljAiIiIDQRWsIiIiMjz4Hhnj5m6ft5bAAKbnFgGBY2iNQWAc1tTnbm3dUJurEu14OK7DmtbVYHPHSwQOuyejXd7C7jeMZHqmgdHpaWXrdtl1fN9eVzRXqeoE4YmuDDFobiaz+GUOTI1gUjrOcVsb2L81Xtgue801nf1XARx3yFawGscpm+RKLQJEqmAD/NIWAUW/9yYex7UlFaxmYAJWPA8SnZ9BjjFlVa2DxTeWpaNh6Who/HHjdjmniIgMH6pgFRERkWHBlk5yZUw3LQLKKyP9ogovE+4GiuNG8n1QfZL5UzQlIb1tG4wfUXasbG2MdKS+4rnHTRzX00sJMZF8wJqvYJ2+Dqa1GSav9Wk+eCG3rsut/8dXctuf8aoLV+V+9spaHThEnCH6/bvj4JRUsHoKWEX6LrChFgEYE6q62X3c3jjvZTD5ENaxBt+NwwBUf1aqYK1Y1SoiIrKTGaJX0CIiIiIDzPNCPVgtYMrm0s4pm+QKSEcStNQdwIb6WWTdcDjqRnKXVG4QPl5269aKx8+MSJCJ1AJQnwFD7mFtwIjGURX36UpHwOrazgrWaKIGP547/thMbn0ykqA9WotjDEEyN9GMXxqmOu4QbhGgClaRgWCtJbDh3/PiCtaMS6gHawSD6aKCdf5v69mnxbBPSy8/NypUq9rt1CJARERkMKmCVURERIYF64cD1u5aBLhYDmkficVAHFrbN/PsnrPx8lWn7ZEp0NpZxeq4ETJ+ptCDtUNXk1wZPyAZqaEun8fW5fOFSGuK+kQfL8/c3DiM7awCWzbWZUN9I9H2FJg4m2r25NFpswiMw9SN77B/sh1qasmuWQcj9yl6HUM3YDXGCfVgtQpYRapTqUWAKQ5YDSODzv7UEWvArTzJlQXGezF8LG/34tfR+h5MmIA1yzsXqoJVRET+DqiCVURERIYH38dzilsEAF1UsEK4irXGg23xzqrVKRvJVWLlq7HcSC7kdK1PjQe1WYg4EbKlk0jlGQueG64Ic9IZJn/wTp9v0TeOw4RsnLifpS3Wudx3ouyXrsOLuDRmpoN1cCy8M3oKre1JADw3fK6OStwhyQkHrGDxK1Qai0gPbEmLAMIB632f/wMJk/tyyA98HAx7ZZuo98OtUQCSo+uY29bEh9vGcOSmnvu0Vuq3ur16sIqIiAwmVbCKiIjIsGA9j4zbGRDkKli7C1gDfNOLwNH3ieQnmiqrYM1WDg4C12HWmgTvje5cZtJZjn7j6Z7PV0EqSOMU3dIL4Dsx9szE2VTTQMyP8lpHa1dj2LA1ySjAc8OXgpFIeYAyZDhuWYsAz69cISwi3QgCguI+0sZQXLhu4gkm1oxgtdOxjUetqeeozGSs72Pyn6NBWyvZuhiQ++yZuTWBv2Uz7qiGrs+dD1Nr/dF8MOpgvGgb29JZyjtVi4iI7FyGcJmCiIiIyAAqaRHw4b3+gVs/djPzz5xfcfNKE111JTpiZG6fkh6sXqVqLd/HuoZUNHfL7fR14GQ9Dln6Mq7fdeDbnXWNMXwTvs3Wd3LlrJtqynu6tqc9bOkkUb5PNDKEv3t3TEkFK/gKWEX6zAYBoe4axoRag5h4nC/P/AxukOKAVV6uD4CFTSMOw1uzsrCdv2VT2bH9DWu7P/d117K83fBe47GkIyNpje/GbzfWku6i2l9ERGRnoYBVREREhgXre/hOZ9WWcQxRt+t+oy/Xb2VRfSutmVYA3IBC10KLBWNyj1GjiMSivFWbwrUlk1xVCA1sOgmZDKlI7nbaZAScthQ16WTVr80zFtemQ/Gj5yQAy6qR9QTFV3zW0ppM5yf9CgeqQzlgNaUVrChgFalGae9iA6EWAcYY3ESCSGZdaLtkrBFv65bC82DrFshkiASWSP6YtrSfanNz54PcnQPP7zY9tIkNApasqTwhoIiIyM5CAauIiIgMC9bLV7B2BKOO02W/U4PBEA7v8kVc+E7H886QIhpxmd+wFacjYDWGA1f7+HfeWQgWOgTt7QAkozX5Y1qwltp0G9V2FJ0wclecIEN90WTc1jhknQTt0brwazCwLZXFetlQ4IzrEo0N3YAVU17BOiRaBJQESCJDXVD6SVPSIgBybQKifituyaabNm0r/GyTbeUH76GfasqNkTT5OwmKeiiv3lThWAOo+c7mqj9fRUREekMBq4iIiAwPgR8OFI1DpKsKVgumDy0CIhGXlGsJSgKHrCnvaWqT7fjGkI7E8wssBkhkkwSlKUcvBQ7ssi1F6d5ba3YrnMexFMKS//zb3VgvS9YpqWCNRhmyHJfSdzMIAgJNdCXSJ4FvYdGi3ANyAWvJZ4+JJxjd+nbZvhu3tBZ+tukUAI41ONYwoX5CeQUr0DxzYe5xZzPtGzaGgtUOKze1kR2sL0yam2HBQuK+YWZbHXO2jmR0Zgj3mxYRkZ2SAlYREREZFqzn4RUHrI5DpKtA87prMSProb6+V8eO5Cd9KZvkyqkQsKbaSUYSnc/zYUO9l8GtsH1v+AZcW145lnXqOoPc4jHYBGSz4Umu6uuJRodu6GBcB9eUBDPWlt3uLCLdC669FrZsyT3yIatrwp+FTjyBa1M0tbeElm9r62xl0hGwFrPZ8oC1WMpW/vPTy/psSXa/b3+0pls5un00H2obyf6pOk7+IE5QYfwiIiLV2m4BaxAMgVu4REREZPjyfbyiik1jHKJuF5dCxmBKqqw6+pi2lRR5GigEtU5pwGrKA9wg2U7K7Qw9bWBxgoB4Nt3LF1IuyJ9n6oZ3eWUctEahNQaZSD3pSJxk/mUX2hvYOH5JBauBrt+PocA4ZS0CFLCK9F1Q+rlkDKWLLn7mSrIO1Ke3hZZvae38nApSubB1db1ldb3l/S3vQ4UK1gJrScYSldcFPq3p7tsL9MeIwGWcHys8rzNx/LWrB+18IiIy/Axqo6329nYefPBBnn32WTZs2ADALrvswpFHHsknP/nJoX0bmoiIiPxdsb6HF42ByYeIjum6gtU4bE1vJh0x+Lbn2a3dfKuBSZs83ABy3U5N5QrWbJZUJPeHfq0HJm1pjWd5Z49ctexBfX1hdIa/41o3AJMLy9ORelwbo6YktzDW0J7MlI1vSAesjkOk5F+XtRZPAatIn/iln0vGlFWwpiNQA9SnW0PL308ZvFSKSCJRsVq1rEXAwoUwJT9RoIVUtKiivvicQcC2QQxYR6c6JyksTFbYjy+1RERESg1awJpMJvnhD3/IihUrABg7dizpdJpVq1bxwAMP8NJLL3HllVcSi8XK9nvwwQd57rnnaGlpobGxkWOPPXbQAtk1a9bwm9/8hsWLF9Pe3s6uu+7KKaecwtFHH93tfsuWLeO3v/0tb7zxBplMhsmTJ3Paaacxa9asAR+jiIiI9J+f9QhMUYDYTYsA45iue7AaAyWVlBHHMGePOWxq3ITv5CbJqktbvOSWXMBQzMuGb9u3locOtPzfabMAmN+3l5U7v58bz4h0G/WZXPWqb2DPLXX5wDfMsbC1PZ2b9KtItMoesNtFhUmuVMEq0nehz8G80h6s7VFDDbDL1lUkowfm9nMMKWtYtnID06buVrFa1VaY5KrVzX1JdePVfyW5x9zQugNX+7BxEXbBK7Tv843qXlAvJGzlL7tEREQGyqCVKfzqV79ixYoV7Lnnntx4443ceuut/Md//Aff/va3SSQSvPvuuzz44IOhfdra2rj00kv5wx/+wNq1a/F9n/Xr1/PAAw/w4x//eMDbDLz77rt873vf46mnnmLLli14nsd7773HLbfcwgMPPNDlfgsXLuTiiy/mxRdfpLW1lWw2y5tvvsm1117L448/PqBjFBERkYHheeFKVGMcIl22CHAwfZhzOuLkjhPxO/9gf3UcPLynofm0cAWY9bKkYp0BqwG+//4E5p85n/lnVhOvdlZkFVebmbIpr3KmtcCMt7bQ+q8Xk3I7v+g2FhJDuQerMThlFcEKWEX6wlpL4IQ/94xxyj4t/vOMXwPw+J6tJLK5OxE7ekS/u25z7lj5gNVzErTGduGTr9XCVVflJpWqwBgTqmBtj1istbRmcp9bKa/nuwWqlQgMFkNrfA821u5Pm41WnJBLRESkWoMSsL7//vs8/vjj1NbW8v3vf59dd90VyP1H9bDDDuO0004D4Kmnngrtd/PNN7N8+XLi8TgXXHAB//M//8Ntt93GjBkzWLx4MX/6058GbIxtbW1ce+21JJNJxo8fzxVXXMG9997LNddcw9ixY/nNb37D0qVLy/Zbs2YN119/Pb7vs9dee/HTn/6Ue++9l4svvpja2lpuv/121q1bN2DjFBERkYHh+SV/vHc3yZUx+EEGP/ALk1B1xdAZTEZbt+QW5vexJootvdzyPFIlFawJv39/6L/dlDt/3M9g6MUX0ga2RRPhcWCoGcIBK4BTOtGVtfg9/PsZbIfu93ThITLkWVvWg9VxDKZkmTtmHG35ytNdtq4EwA98nlnxDJvb859Xnsea+jGsGH0Ca0cdzvyp/8DKmoYuT20cU/KZU7wS0tlBmrNj4UIagijr6/dhQ/1sttTsy5qRx5BJq0WAiIgMnEEJWF944QVc1+XEE0+ksbGxbP2ee+4JwKZNmwrLFixYwML8LXTnn38+c+fOJRKJ0NTUxEUXXURdXR33338/ra2tZcerxu9+9zs2b95MPB7nkksuYd9998VxHKZOncpXv/pVrLXccccdZfvde++9pNNpGhsbueSSS5g0aRKO4zBz5kzOPvts0uk099xzz4CMUURERAZO1i/5491xiLhdB6zFLQKSkdzDr3TlZCERya2IehkA2vN33hssNrZbeHMvSzoSC+/fz4B11UiHNsfHAG6Q6cUehtb1LaFqsuKgeMgyDqERqkWASN/YoKxFQGl7AADjRvjj6E28H00R97aE1m1OZsl4Ptb3WDx+GrXZCPUZsI7Dc+P26bL23xiHZCSen1TLEOTP61sfCySzg1PBaltbGeslWDdi/8Iy30nw/hZVsIqIyMAZlID105/+NHfffXehUrVUS0sLAA0NDYVlDz/8MACTJ0/myCOPDG1fW1vLhz/8YTKZDC+//HK/x+f7PvPmzQPg+OOPZ/z48aH1++23H1OnTmXp0qWFybkAtm7dyvPPPw/AqaeeSn19fWi/o446isbGRl566SWy6ukjIiIyZNggwCsJWI3jlE3sUuA4kK8EXTzBYA3YLtuTGuLR3CVVzEuXrDFkRxwTCgGt55UErJaE35tQtGu+Y/jf+vVENm3rVcBqgG2JurJqsqFewUqFClZNciXSB0F5BavrVP6TcF0syzO1m9hi10FRZbwfBKze2AbAhrrRoX22Rmv4oIsqVmPyFazWMnNNruWAa8EJLGxsGbQWAUHUJRubwLSW3ERbbv4jY1Xb4E2qJSIiw8+g9WCNRCIVJ6XyPK8Qph566KGF5UuWLAHgiCOOqHi8gw7Kzam7sHSiiCqsWLGCtra2bs/XMVlV8fmWLFlS6ANbaT/XdZkxYwbpdJo33nij3+MUERGRAeJ7eCVdBqOuW3ZbbAdjHBzb9R/fvslN+ILrwtFHF4JJJyjp85r/Q359a1HwOggtAgA8Y1kWS4LtDFhruzqsgY21DZ2ziRsDxx9PTXTQLg0HRkkFq1UFq0jfVKhg7epzsNbLdXK2Nksi23nnIUFAy7Z20taQdSJM3QhTN0JdOve7+PbI8WXHuu0hy62HBLRG44U7AQ5c7fPWaHhrNPy/PbODV8EacVnRMKbsDoS2tAJWEREZOJHtebL333+fu+66i2XLljFu3Dg+/elPA7nK0I7Ac9q0aRX3nTRpEgAffPBBv8exevVqIBcCT5kypdvzrVq1qrBszZo1AIwbN65i64PScc6YMaNP4+qo7O1OQ0MDrjvEq0tERESGGOv7eCU5XCTazWWQMZhuAlaAZNylPlZPXcQwpq4zMHWDNJB7bgCsJVUUHFgvW1Y5mvAH6A99a4kE3fcVfGs0YAKCdIR3x+VHOWoUhzjukK9gzVUdl/Rg3ZEBa2MjNZ/M/btLJrbrZbVIVay1ZQGr20Uv6mTCxeLhGcs/vLOFd0c3YYwPQcCGbSkaAif35UxRUwALrI+H7/Jb0mRpj0LcJsom2OrgYMh4AX5guxxPtYIR9XhOTdnyTenBm1RLRESGn+1yJfj4449z3333FQLEqVOnctFFFzFixAgAtm3bVth2l112qXiM+vp6XNcdkAmkOs43bty4LsPKUaNGAYTO17HfxIkTuzz2yJEjy/brrfPPP7/HbW677Taampr6fGwREZFhzfPwSytYI91UaxrTZQXrjDWW+gzEghS7eGmOnOAUAgFrLRO2LubtMbm7dNpisKbO8oXfncuTX8zNyl3ag9UMUAVrx/lrMpuAXbvdztAZ/gLQ2ko06hJxh3gFq6MerCL9UnGSq8q/93P2mEPr6qfxDTQkN3ceIghYvSXJLrakEhagtZXNW9pDQWnH2WpNLV01aG30c3+Wpj2f2tjA/omaHF2H79RQl4HpHX+iGWjfNyDrB0SH+ueeiIjsFLbbf006bq2HXKXmsmXLCs+L+5XW1tZ2eYza2lra2tr63d/U83J/MNXV1XW5Tce64om4Os7b3X4dfVmL9xMREZEdy/oe2T88BL6fe5C7k6VLPbQIOGzFy3zmlT/x4TWLGBUvivyspSG5ovMw+WjhrZZ3CsuCrEfajVGfATeAWOBQ088erLlzgQksNdnNhWXtRd2aAgNtMdOZdpRUidXEyls7DTmOg0tJBasdIgGrr2o42QkEAQE9T3JVzDeW0ckW3hkNbzda5i17hGQ6y7Y//aWwzZtN0B6Bl8Z6/HR2hnE/3Y3mO5sB2KfFUJuFuKkvTBiI6xLL/02WjEIqYvj5Ez/lo//zicJ+A/Jy29vAQI2XoPRV2iBga0ptAkREZGBslwrWuXPnMnfuXN77/9l77zi5zvre//2cNn27dlUs2aousqyVccN9KYaAaSE/UkgwhJjATcglcAkkAS4/OwmQXAj5cUO7gdjJBQIhQCihswbLBmxsrausanVt353Zqac8z++PMztlu6St8vPWa16aOfWZnTlnzvk8n+fzPXKEr371qzz00EN89KMf5d3vfjdXXXVVpddUCIHjONNuZ/xGyHXdKfNd58r4/ua6r3Ndb6586lOfmnWZ2sJgGo1Go9Fo5kgQEEwYFjujW9OYOSLAlDV+WHPy5VSidBrEmoqMIQivW5RSYSGXm28OZzz2GDQYRA8fm+MbmZ3b9+U42gqFmAlM8R4UKFRF/B0nOs+usYUgjAioeU/awarRnBlKhfnRNUxX5Args91JfAs6VAlTQtaBTGGU/3jqq2xwkrW1ryq87im45umA9endKJJsK8WJNFokqY8OiHpFTGkD4b1Vu2vhqfnt6JGZUQB8I4xlMcfba5kQBJwYLdCamP7eTqPRaDSaubKo4yEuuugi3vWud3H11VejlOKzn/0sUkoikfAHb7rhKeOMzy+VZs4Wm41xgXSmLNPxsPdaoXS8nWe63lxpbW2d9aHzVzUajUajOXOU7+OL6m+oUGrGYaFhkavpR8xE/QDbV9DbGxa6Gt9P2U1589FQBDTU+BDZsmjg+xRljbixcyfit36byA+qTrBzJebm6icIUX2EjWHbMMQCgSIs2JU1A2KRFeBgFRMjAiS+Flg1mrkjJxe5msnB+s73dvLnt0dxAo/x8f2mAkPBiJOctPy2IbCk4K6uGG9+uc8THZJdxUbMWJy+hsvqlo34xbrM6Mai4tFTT7D72O5zeIP1qCAIz3NG/fktkD7IgL5Mcd72pdFoNJrnNoseOCOE4JWvfCUAo6OjHDt2rDKsPggCMpnMtOtms9l5acNchvGPF91SNcPOznY9jUaj0Wg0S0zg4xv1nZTWTMNiDYFQ0w/5toMAoRQUSwizPiIAwJJVcVYoEOVBQyrwKan6/TqONa9FXUwpsYIa0cA0wTQpRE0smcOUYZsmEo+sABfXpCJXEMgpLHQajWZKlJLICe51Q8x8/smbEgOFKT0CAcmSYjg/yEhkssAKYQdVu2ymxbex42G02r5VF1O0G+qWS7rZclHAKpaKnulbmhHlubimDTXveV8rHGyB3UfvZ6w4P/nXGo1Go9EsiMAaBAEnTpyoy12tpbZIVLFYJJFIEIuFlR2nKw5VLBYrztVo9Nx+eNva2mbcF4Ti78R9ne16Go1Go9Folhbl+/ij6VAAVQrULBEBiIqDdWevCkXSGl3PlGXx1RB1DtZxLFkVZ6PKqAiseB4lZcADD4SPxx6bf+eoUjhBfspZEX/6TuJ4PDK/7VgIJhS5UtrBqtGcGYopHKwz3xIWzPAYy9suSQ+SLqwZDdiTzJG1FftbwxxVWd6MFYuxzmjnKtlGSwEMKdjbsb1+n4HkomdPUrJKBAKC8rqt6qJ5eZsVPI+SWe08SrihA3ecbOHcRkZqNBqNRjPOggisf/Znf8Y73/lOHnrooSnnDw4OVp43NzcDsGnTJoC64le17N+/H4BYLDZjIay5sH79ehzHoVQqcfLkySmXOXDgABAO2x9n8+bNAJw+fZpCoTBjO2vX02g0Go1Gs7SowMcPaoU4NYuD1cCYKr+0TEVAlQpRm8FadrAaNe7X7fkE0XKFbOV7FO79V/D98JHNEp0HgbX7jm5MJTBUOIrGDnJcfjog7lFXfOlVTw8DEPPDLEJBONQX06SteWo32nJCTCxyBTqDVaM5E5REGmdW5CpvShRww/EiKNg2CNcO2KhykaptQxD3yucSQqE1HdvCs62v4uELdpJ3WoBQRB1/jIgSzUMDeHKg1lxKg1yDoZrm692GDtYZTO7FkofUIw81Go1GMw8siMC6a9cuAL7whS9UhszX8t3vfheA9vZ2Ojo6ALjiiisAePDBB6fc5hNPPAHAxo0bz7l9tm1z6aWXAvDAAw/MeX9tbW2sWbOGIAj45S9/OWkdKSVPPfUUUBWMNRqNRqPRLAMCn8GYUbm5F2qWiAAxe5Gr6ouqp9IP/MnzgU2FcGSL8uvdVEIpYvb85KsnAzPUKZTCDgoY48JjWTyQKBpK6UnrCSFIOEk6WlLz0o4FxTAxaz82JZdOYO3qgnQaU4KhUwo0KwUpURMiAmaLKBlywvNZzK03mAQiTGVVQN4GKagTS5Uw2b9qM4fbykX9ag5V6Xk05UZpHXkWUT5fmhK25CO8KPsC/OzY2by7SSjPo2RYCKBgVZ2ylflS4vr6ANZoNBrNubMgAuvLXvYyYrEYfX19fPCDH+TJJ5/EdV3y+Txf/epX+elPfwrA6173uso6N910E4Zh8PTTT/Poo4/WbS+TyfDjH/8YgCuvvHJe2njLLbcAodg7PDxcN+/JJ5+sOFgn7m98va9+9asUi/Wh6Pfddx8jIyMYhsHOnTvnpZ0ajUaj0WjOHeVPyGBVzFzkyjAwygKrApKeIOmJSkyALavi67iDtae3B1UpAlN/w97ohy5V5Xl4E/RAZ74LWE4TEeAbEPOqAklgwOX94eMFFz2P5IoociX4yeEf8ljfY+FrpR2sGs0ZoRRB26pKNjO7ds2awRoIOFkaJuZVzyvxaaJLJ043p9EuG9N9JItZimRoKB6om5dgFXt/+KN5qWmhPLeuU2sSMqCkBVaNRqPRzAMLIrC2tLTwp3/6p0QiEY4ePcpdd93F7/3e7/HGN76Rr3zlKwgh+M3f/E1uvvnmyjptbW1cf/31AHz84x/n/vvvx3VdDh8+zF//9V+TzWaJx+N0dXXV7WtgYIDXv/71vP71r+dnP/vZnNt43XXX0dHRQS6X46677uKZZ57BdV0eeughPvaxjwFwySWXsGXLlrr1XvSiF5FIJOjv7+fuu+/m6NGjFItFfvKTn/C5z30OgBtuuKESfaDRaDQajWYZEPgEoi69c2YHK6IisE6FFdTMm6LIlTGhQJYav+TyXHxRv7xjzV+Bq/G9JUt9k6aa/uk6gaSWC1qWfzwAgDDN+r/tUjpYx5tAOCT64KbGJW2HRjMXlJJIISCZhGQyjAmZRWAFyCiX5pHjZ7VPMeH/xsII1+z7CQL40H/5bBvYR2PBBUIxNxDwN7/8IcX+yeexM8Zzca0ZBNZAC6wajUajmR+s2Rc5Ozo7O/lf/+t/8Y1vfIOenh5GR0dJJBJcdtll3H777Wzbtm3SOr//+7/P8ePHOXr0KJ/4xCf4xCc+UZknhODOO+8klaofvqaUwvPCrtLpimpNhWVZvOMd7+Cv/uqvOHXqFB/4wAfq5icSCd761rdOWq+hoYG3v/3tfPSjH+XAgQO8+93vrpu/atUq3vCGN8y5HRqNRqPRaBYeFQT4NVmps0YEGFNHBFw5YDFm+tjSRwEChZjCgWpOuCZR5dJMyvP44SaTlvIgmAYPrFkKzJwpSioykRy3Hv0FT3dcB0DUL+H4T2IqWY4PiFUK0qAUq7ZumX6DywkhMKj52yqFv4T5iX7UZrMTVkY/OjY5FkujWXZIVR8RIMSsGazjNI9OXbtiHENJwMCUoUg6jqr5v6GY4cYDP0b6Jfa3CQxLYqqA9rG9BMZOsmUt1MPkkWeOc2PH6jm/tamYGMsyab4McP1g2vkajUaj0cyVBRNYATo6OvjDP/zDOS+fTCa5++67+dKXvsSPf/xjXDfsyVyzZg133HHHlPEA7e3tfOUrXzmr9m3evJkPfehD3HPPPezZs6cyDGX79u3ceeedrF27dsr1rrzySu6++27uuecennnmGSAUgK+++mre/OY309ioHQwajUaj0SwFzR+pjiAZec9IdYYfOlgL5Ssf5xWvwZohIgBhhOKp8nl8tcXNJ6oiqqlUWUgAEGBOvpya5GAtV+1Wf/gWgi1tNTMUtjk/DtbuQzdCsYdCKQbA2swpdp36Go4yMdav5yfrMgA4QQ6IVf4WUkkaO9rnpQ0LjmHysi0v4ZFSueCpUkvmYJUo8mtbaPfCz+/2oSaU5yHsFRC1oHkOowhE7blPMEd9lT+/ocQlbh/72zrYNgQFm0pHjSkLrBvtZyRxIQBJF7YNh65VQRhJ0lA6ybXHHsfLjzF+RpXl0QC/8/hR/vH528g6MQRgGiYHBrLcoBRiDg7bad+t51K0qwJrzoGsU87ilgH4AaVAO1g1Go1Gc+4sqMB6NkSjUd70pjfxW7/1W5w4cYJYLMYFF1ywYPtbvXo1733vexkdHaW/v5/W1lZaW1tnXW/Tpk3cddddDA4OMjw8TEdHhxZWNRqNRqNZpqjAJ6jNYDXEjA5WYRg4poMtqF+PsIBV7ZpTOVgNFVSKqQQGyHEHKwop6p20M2XBng1Cht7ajizYARhIBFAyQyEy6mWQRlXklYU8banovLZhoRCGgflf34G2cCSUeuGLl0xg9YXkdINAlb8MscDAO3kU56IV4gbWPDeZWORKiFkjAjpXd2Iau/ngTxT/8aIcRKZaymXr4D4eia0j5luYCmI1eazrR48RKf2KSN5jrJAnjsBsCO+dTtpFbBll/cjDDMZvRgkIZMAP9v2Y3yjdSCp69p0W6m/+hoI9fQeSCnxd5Eqj0Wg088KyE1jHicVibN26ddH219TURFNT0xmv19bWRltb2+wLajQajUajWTr8iQKrgTUH56ihfIIJaoIlJ0QHTBBYX7Ef+hP1DlZRk7saGLWXX2rBBNaJ+AKcQNGaO0jUW0fBiiBKBa7Yfz9Re54LbS0UhlFfQGwJHaxKTN6vTI8ufkM0mjNBqTCDtYbpBNbuO7orz/d8OBwdYPpjlWkxLyxipQQY0mXzcJZV+fs43LKB/ljAa584TCbaQsZWXHmyl/ddMcJ1I8VJ+/lFdJQ3jcV59eMeNiWebo8AikxhjNOZ4rkJrAKKVqQSUxDmZIfvV6FAC6wajUajmSeWrcCq0Wg0Go1GM1+oIMCvETaFMGbOPi3PmyqHdaLAKpyq+zPnCBxTIFT9Dbuhyg5WQZ2DldmyYM+GmkxSzwRTCcR/+29YD74HgKif5RV7f8DxxgTmyaMY7tSFr5Yly0lgnWKazI1NMVWjWT4oKZG1EQFibhEBu/aGkSupb36Du+/7Ud0BIEwLW/oIoKmY4cpTT7K3FaI+NKZP88e/BM+Af/1CkUPNICfs8H//FwQyhyq5tOYHgXUA2FJwerTAtvb6GhxnSsGOVZ4bwXjDBQKB8n2KWmDVaDQazTwwv5YJjUaj0Wg0muXI//zAZAfrTKpCWYAwZhFYhVIYDZMjgiw5wcFa7tNerIiAtqKoZB8CYFqkbcm3toVChxN4XDIwyjVH81w1sIL628VkgdVfRgKrymUXvR0azRmhJHJwENLp8HEGRa4A2hMOjh8WdNs2BElPkHSSxL1B+pOCoTiMxOCHqWGQEkl4rJjpLBNN37v2jrBr7whXbbuFKwctrugXrBkbrMwXwOnhc+u08IVgLJKovFZK0pJPI8pnx58+242rM1g1Go1GMw+soCtqjUaj0Wg0mrNDAYGoF1hnFDbLgoNBvcBqBJKmTCG8NVcQ7U9PKsDiFguschOYNffsQpj4UobtMGqGuypFxFqIiIAJ00yLH73/EH/35i0VkcNOl52ryeS87n8hEZMcrBCo5RMRoHx3CVqi0ZwBUk1ysJpnUETKijhsGH4AjNvCCaaJdWUnI/u+Eg65B9xCgb/+QUB0ZAg7EsEo+bheCQG849fAvOUWACoBBN3d0NWFQNGaH6jb3+hYgbzrE3fO/LZVKUXajtV3hkiFL4ZQNIXNl+iIAI1Go9HMC1pg1Wg0Go1Gc94joS4iADFzkatx7+dUDtbo8CjxE0MIP8Ce4sa8WMgRida7Wg0lKHkBkgkRAcyzg7WzEyEkw43HsYPxrEOFcCIYsTgv/vkw//b8OFY6IDI0BudQnXtJMMxlExEwEakUyvNmX1CjWUrU5IiAMzkNCCdKJMjy8r1fZ9+qS7CtZi5pVXxg1QD/PVNCKUW04LPruMdYRODkw3Po3tUCTJN/+CHs+nz31NtGkMqPYEifLSMW4KM+9UlO7/pbNredRUeQ71EcSUM5YeBgC/TaLutK1RxYUylKWmDVaDQazTygIwI0Go1Go9Gc9wQnT9a9FoY5Y5ErYUwTEaDA9n2sgovp1ccAdK7u5DVvbeSVfxAncaqfy/sh6VIZiloqunimiapRM8RCOFinmhYt58QWS/wiOoI9lAmdrqYJnZ3zuv8FxRD1AivLI4O1ZDXhmo1IVwusmuWNmlDkSnBmDlbhOAAYSnFp/15uPvYoa5sTBCJ073ulIsF4RErtdk0zdMtP55jv7obuboRtEfeGQMH+Vvj6Zpeb//eLz/h9AijPpWSG7e3shc4+wetvezuGqh6nueKYjgjQaDQazbygHawajUaj0WjOe1zLIjk+elsIMGfLYB13sE4UzBRWML2I1rm6EwC7cD924DN+qRXIgNd9+be5ZW1/vQCqwJ5B6D0jusuusPQovPfKup0Iq74K94EW2DoMqfjKiQeAUBg3VVXYVkta5Crc71DictKxbUhD8EjW5AVL0hqNZo5MKnJ1ZkZ2EU/QmxS0FMrHnXKxVq8rn/vuq1s2FWuEIMwl3pVNwpbOWbd/0Uc/h/za/wLRAYCjDCyjfe4NrEF5HkXLqZsWi8fqzuumgpIfTFx1ZdHVVX3ePbU7WKPRaDQLjxZYNRqNRqPRnPd4plNVERobwTBxZsxgHXewThZT7cBHljdlNE4ucAWE2ap+idpLLVM6GMJhZ2/NbpSauR1ngbDDfXrlyNlIIMCqtuM7Xwj/T8WbYGRkXve94IgJDlYlCaRCKTUpC3fh2wJFM0I6tpWgHMr7hSNP87yCS2PMmW1tjWZpmCSwnpmD1Uw1YghBrYfbXr8RHp5i4Vp3/ByFPyPVSNQfrryOKANTNBFIhXkGxbjo6kIJiRtUs7eFUsSiEQxZzUo2laDkrnCBVaPRaDTLAh0RoNFoNBqN5rynZNcLXqZpznyzLqbOYBWKaR2s3Xd0Vx4oRcyr5vwlXIXYf5xb9tdVvsL+k7fPuzAoLBs3cFFUJRARjc3rPpYMw8SYUORKoZak0JU6cYLheAsTQxlODJ5b1XONZkFRsi4iAATGGQiXZlsHRaN6DBquj0im6L6jm7gviPvndj4zmlpwgnTldUpa2MpkJH92BeSKTrT6oqmJaNSZ1HFWcj3UEhXL02g0Gs35g3awajQajUajOa9RSuFOGCLv2ObMwua0EQEQLxUolDeXmia/VABxNw+0VqZZIoFv1gi2pknEsSete87YDgWzRoQ0BM7GbfO/nyVAmMYkBytAIBXzHGU7h8aIssBaz2Cm/nPXaJYTagoHq3EmGay2zfda03zAjYNUyMFM5Vz64j+pOvrP1htvNjZjKp/2bI5DzQkALipGGMq5tCUjc95O184e1ro2t52+EZqawonXXEsiHsFQVbE2kAGB7+FLNX9xLRqNRqN5TqIFVo1Go9FoNOc3MsA1JwqsM18CibIAYcrSpHkDSRerNbwR3zXDsNd4KV+/TbMd38jVTbOd+b8UE0LQ0yoZH6gUGclhJMMy2tKoH9q74hAGpqx3sAJLksOqBAzF64XUrJtlKDv5O6PRLBuUqiu0FwqsZ7aJoQYH++QgAEaNg3s8g7rCWeSBGg1NFKVLU2EUCAXWBiL0j2a5uCN1RtuylKBoVR2swjBJRCNc0NjBifI00zAhCCh6AfY8x7VoNBqN5rmFFlg1Go1Go9Gc1yjPxzPrL3lmE1jHFYdIMMGHpRSJYiasiD0LazJ9wMXVCWYr6ehQ3TIRZ2GyOp9OlXCOZkBA1K2KBoWoyb62MG/wqpMrLH8VwJjewbrYSGEwGmuaNH04W0QqdUauQI1m0ZCS4BwcrBAKqWJCQSsIY1LOFWGaPB0vcL0aITDWAWFX0ekTvXDxmjPalqUERTtWlYBNk7hj8pZr/pCHHv45CoNABqggoOBJUtGZtqbRaDQazczobjqNRqPRaDTnN8FkgXXWofnjDlbl01A6Wpm8KjdCqpCebq06OjJ9mNKrVJuXvs9AYlV1F8kkjj27UHu2CCkRgZw0vRA1KUQXbr8LygSBdTw3cSkE1pwTxTcs4l5YiVyUZRzfD8gUps7p1WiWHKXOKSJgnMc7wse+BUjD+HFzhpbCEAULChb40mewfwhvivPZTIQO1mqsgDAM4o6JEYlg1I5OCHyKvi50pdFoNJpzQztYNRqNRqPRnNco38M16gVVezYHa43g0JZ/nJuODeIZFpccP8ihOe7XDCQRP8N4HqeNIB1tIBZUtx3RQ1LPCGEYmCgMFBIBZYHVX2SBVSlF0Z46D1IFAUN5l6b4wriTNZpzQSlZFxEgAGO5nYYEWANHgTDqRCAI8nlG8h7tqbnnsFrKIRA1nUmmSdw2MSJRHCSuEc5TnkfR0wKrRqPRaM6N5fZzqtFoNBqNRjOvKM+rc7AKIHIGAqtAsSkacLFTwrnoorntUymEUthBoTLNUoLAqN9vIqL7us+IcrEyS5bFkHIe65k6284ZGVCyphF6pCRb0mKNZplyjkWuFoMbN9yI5XtYssiOfriiV6Eefohsae7O8GwhjetBzFOQzYYP0yTmmIhYDLOm0BW+T9Fb5HOIRqPRaM479FW9RqPRaDSa84YtR7OTJ/re5CJX1syCgjgHS1fXvV18E6BGYDUlRFR5n+X8VvH8G0guoMD6xOpwfzelk5VpSSc53eIrAmHZcOQooqGfwIlXhFY3WOSIgCCgZE7tUFUyWHzBV6OZK0qdcwYrjBfMY8HiRqSUWEGegy1xAIQa5daxHLTN/Rw2lEphFEApH4EgHrGxDAMvGsNU1YgA5Xs6IkCj0Wg054wWWDUajUaj0Zw/BJNvkpXv4U1wjjqzDc2vESB+76ES5I6FL9ZthOQcb/AnOFgvGxQoAVnDByFoME0aovpS7EwQlsU/dyr2r/Ip2sCpR7n2ss5FFzRVEFC0HJIuXHkasg4EJkAAMsDVAqtmuSLrIwJAYC4vAysAH/jL57Pu8UKlQJUCsmP5M9qGKRqrK6NojIWdIkY0jiGrDlZ1HjhYu3b2hE/u7ZqXYmMajUajOXP0Vb1Go9FoNJrzGuX7eBMdrLMKrGc0eRK+wSSBVQhBrc/StAxWNyxM2eqsm60UgKrlne/trDxfibfgwi4LJCocKiwAlFx0QVN57iTRvkIg8fyVLdZozmOUQja3wLgDW4TnpuWGEgIpc3X5sNliafoVpsASDQRGeJ4wJTQmwvcsolNEBGgHq0aj0WjOES2wajQajUajOT/p6gr/FwGlpo2VyUKBY83dwTphxpx2/dhqQTwJtxwtsvcCC/AJDDDUeF0mRSpiY+siV2eGHQrlpvLJOiDcLF/o+Veu2/ini9oM5ZbwpoklUNrBqlnGKCknRQSYxpkLrC/+/TAaIOkkGZmvxk1AUuNYFZAtuNMvPIGGwMKhoWZ9QVMiFj6dkMGqfF3kSqPRaDTnjhZYNRqNRqPRrHzKYmpso6Jgh2LB+JDJLYUIzx+5pGZhQdSeJTdwXhxdigtHCyRdmEqKS0XtKaZqZkKUi1yN2R6BAKSPoaC02A5W161zRe9rJVTugWsCncGqWcaoyUWuzkZgHc9z7lzdOU8Nm8yGxnZ6x8LnAkG25M9pPe/kMV6SbaOnsT7OpSlVFlgjMW5/KsuvLghFVbXB1wKrRqPRaM4ZbZvQaDQajUaz8unpgd27MWW5anQNJoKSVS1IJIDYLAKrECIU8CZOn2MtpRf/vsmfvURQsIvgTy0KpOLTVKHXTI9popSqFKgRCOxAUXAXVxxRbgnPrPcpbBsKH3R3L37RLY1mjkxysMJZFblaFEQRUR41kLcUn/zl/6H5Iy3TL9/VxZ71Ngde+jze0GPhm6GgOn40NjUkADCiMeKlEgebFAebFI/17qFQnLs7djmSdbNk3Sw9vT1L3RSNRqN5zqIdrBqNRqPRaM5Lsm4WANc1wrzMmgJY0dkiAoBAKEx19sKDJxSGUghZRBk1WasirMDdkFiY/NXzGSEEGcPHlMXKtJivyLlzc7bNF6pUxLOmdiArwNUZrJplipxYCFCceZGrhS6iNL79gR/+F2/98X9VpssgQDDzeXNzv89oY4Ivd95eN12gaEiFAquIxXACt5L4knWzFIsuUqnlKzZrNBqNZtmjHawajUaj0WjOT4IAggChJjtFZ40IAGTtfbbjhI/Pf77iFBoXcKfDFwrPUJiqWD9DgSlMGrXAelb8WbdHR7aAqcCQipgHmeIiC6zFIq5RL7Dubwkf9yeHdAbrMqL5I82VhwYCWe+uFmcZEbAYxCMORk3AiqkEBrEZ11G2yaMXXDFpekMxi+WEx6wwLeKiXmhWvsfYIp9HNBqNRnN+oR2sGo1Go9FozitMJUgGJlkzvIF+wxMxHttWvpk2TazbbiMyRwfrxKJWwph737RfzhO4+qTLgxsmz29MziwULAQL7TxbDEw7wksOjrG3I3ztZ0ZI50ukCx6NsfnLte26t6vyfOLfTRXzkyICxnGUwNUVyTXLFCknxlcIjGUqsBqOTUS5FEUUhSoLrPEZ1wliEfa3rcWW1MW8rBnsRdS4UzfF2nANl8sGHRQ+6qtfZfSq987rOUSj0Wg0zy20wKrRaDQajea84NbX++Qc+NS3FaaQ/PfbFAVHUHTiZMcjWE1oto26G+3pmNKDaBjctOHGGdfrvqOb5o80YzsOAkHED/NCD7ZAoXzllYonaSznAS4ESSfJq/5b+HzkPQtV43tpsEo+SXcEU5YIjNCd7B05xLHNq9gRa1yUNtz9/b/gInMbgREK+ghVCXo0lcArFPVwY82yZKKDNYwIWKbfU9sJ40DM0O1vAuYMAmtP/+M0rVmNb1jY5RN41gHp++w4fqhuWaEElswCYaarEix61Mi80dMD21Zmp854R9b50Pmn0Wg0WmDVaDQajUaz4pH5HNu8BEk/RmljB3GvwDWlYf5h/ShjsTgKFRaoCgLikbld/vzNiz+MHEvDoXurE4UB3bPfCHau7mRdOgAOAR47e2HPGoh7oQ63s2MnTnxmJ5ZmasTAINauNTj+abKRiwD42d7v0n7pxexYuzgCq/PMAdLR7XgmJKUJ1AszMjeGF0gi1uxRFBrNYhLICV1HyzgiQNg2liyA2QQwq4N1w2jA4Q3NxHwwym8z8D227LuPNenBumUNwJTjxfKA4eFFL5b3XKfr3i52H9sNhFEe51tnoEajee6hM1g1Go1Go9GsaJRS5DsaucRrZaP3Mh668FYe2PRSopHLWec6SDMGVYMhcWeO/cvGZHFsrhEB3Xd088+v/VcA1mZOVqbnbSjYcOrE18BauH7uztWdlcf5hhACv1ikoXiaxhI0FiGZ9/nnn36eU+nCorTBE2pSRICpwoehQPk+XjBxKLZGs/QEUkI6XX0IsWyd1sJ2sFT1mDZmEFiVUgSJKIPxlrrpTUPHWD+wf/K2/+bDCFEdXfC9Cz1e+aXXzWPrNRqNRvNcQztYNRqNRqPRrGhkZpQg6jCU2EQiSBHzQQqD0w1X8IrhU7ixVN3yiZgzzZbqEeYUYqo5d0eiKAtwbbkBdvQ+w541lwBgB2MUm0/OKabgbDnfh1u6Yxlibj/5SIAS4WfSXJDs6xtjbeNZZNt2VfNWxx3Kjq/4uw/9Cul58I5mGKm6q0wsavN5czGThlyN+y0IcH0Jk+uraTRLyuSIAFimBlaEZWPJcpFABU6gpi1yJV/QhTINspFq9IpnSJJjfVNvOxrFlFXx1kJgsEILD6bTxDzdoaPRaDRLjXawajQajUajWdHI9CgAmWhY9ShvwZitUEJwqvnXONp8Yd3yidgcb6KncLByBkWuhBOqawLY3vc0l57+BquHv8aB4N/5F/HEnLejmZofNw6RLA1UXsd8OHq8D6nOXWiQXbfywp8dIdLQiLOqneGWprr5QtSL9F65oFnWCR8/OvQD3GDKFF+NZkmREyICTDG3TOolwbaxZJ5dpxVJDxpKAqM0dREqKRSRAPqSocAqDdiTyBErZcMFOjvrljdi8UpEAIClVrDACjzRHj40Go1Gs3RoB6tGo9FoNJoVjcxmAHCt0KkaL8dhdvbC7g2Tl29IztHhOIWYKqYSXadbvbEJRVXsM5A8mBplX6yAJbS18VyxsgWSxUdpL7wMAJlOk7tsiMGsS3vqDP6+zc2QLYswN4YFzDwjICoNvry9maMtN4Dh8JrdT/Di6y4jYpkIUS/ExGMNWJleKAs0hgJPC6yaZYiU1Dnxl2v+KoQRAXaQq7wuOuCbEW6950UIUXWMd9/RjSqfbXNOMixwFUjWZSDpFTDNybe8IhILC2iVsZRArFCBVRqCmzMNNAYmT0Z07rNGo9EsFdrBqtFoNBqNZkWjSgVcw2IkFiEoX9kYM2hbbc3JOW1XiCkuk87EwWpauMXqEFSJ4mikOMMamrliGiZSwAPNAzzdNsajq6GnXfH9J77G8dH8GW3r6tel+dUqn1+tqhaqCo48S9Er0eBdzaYRm81DimefOcz3njpNICXKqBdipOFhSrfaPol2sGqWJf4EB6uxzAVWS+YRZVe6qcK2Kjm5k0wJyDpxIn4opgqlEEBH4JN0Jp/zRSwUWMe7wGwMLKKoeXDALzal1hTbC3EucCO8tD+JzI4tdZM0Go3mOYl2sGo0Go1Go1nRqGKRbGT6ytK1OH6RxsbU7AsCSk6uKC3sueW3VpYfHcNtVBgFk/vjWYpGePN+PhafWgqi2RKxRD/FsnvZ8RSHTo/yvPXNc96GocCKx/nE9Sb9jY+TvbeLb9kGebsRT4TDjRUw9mA3xyNRDq6KExhRxmtYZUVQFlirAm22kA4zWDXLj+aa70ZNru5UObznI5MjApazwGpjIIl5eSCBoURYsFDFgOyEpRX9yTbkeB+YUqBc/vxdlyDE5FxqI5EiJur/Fg2+TcmXRO2V4wJVSuE2JqBmtIR39BCR7Z1L1iaNRqN5rqIdrBqNRqPRaFY0slRkLBI6lBLuzMvevPcHGHMtVBVMJbBOnf83HUpK8mNp7N5h7vquy8WDcMmo7t+eDz77bcGHvucTL50Ig27LOtHQ4AgFd/JnNx3XFZq478pG2s0kW/M2jq+QlkkuuhoIIycEgO8z8pNvc3D/EXyjXmgPTJ8bj/skXUi6YAIlLbBqliHBBIOmaS5ngTU8zlJuKKYKwmMLGaOnt6fygFBePN3QjimhYMGYDW98ZID73tg9ZdE/I57gL37tg5g1f4+IFOTP4NyxHFCF0LGvyg83cJGF3IzraDQajWZh0Ff4Go1Go9FoVixd93Zxy2GXm+P1Q0BzTlj0KOaBFGGP8vXPPoDRf+IMtq7g3nurL++4A6wzE1jHebwjbI+w9KXXfPDOv7oR9vQA8M3bP8SbfvgdlDBRKGRujJGCS8yZPWs3GBpgvRcjEBYl+1I8M866Pb1IQ5CJrqWpXANnXysUbADF7p98mh1GFGp0GN90+dUVKTKBwDRMTKUoeCtLqNE8N5joYDWmikJZLpQ7tJKlLAWrg0DAJfkYvju580IKRX+iNVzeBbNxFTfmZx4q7zS1EPFdIBRybSnIuz4tiTMbqbCUjGeQ/86T5ddCIaJzzBnXaDQazbyir/I1Go1Go9GsaCI+9CfbKq8LFlw4coSmYsCTqy4g4isuPfUUTcf2EpxJvt5UhVHOIIN1nLhXff7Z7lAI3vXJ83cI8qKxqxMAu2MNtizimuXh/LnsnF1o3unjAJxs2slQPKyIJsQ6ui8awDdTTOXtSwYx8k4LjTWf6/+45V34e37BZw49AoCpOCMXrWZx6XpteXj5vV1TuhvPZyZGAy9rB2u5QyvhVuMAEtIknrbJBvURAT6C06kwKibmhze57cXMzNuPxXFkkYrAqgS5FXbcyt/6TWrjAQDwZhnKodFoNJoFQQusGo1Go9FoVi5KYfomval2ki4UrfBWsy03yIbRY1zc9xgPtRS5ZvcIYwriU0pmU6PdpisDa9VqDFmAssD66JGfc3Px+jmtK9/xJ9DqMBpbX52oFMcbVrNzEAgCAqHqhhFbSpB32iBXXT4Wi+DXVFYzlSDnVjNZNUuHzjuuJ5ASklXH/7LOYDVNPEORLJXF1HJT/YxPTPiomqbn/vjt8LWfVddVgpQ3c1FBIx4nUcoDDQA4ErIr7LiVYnKnofof7wZZ/v1aLnnCz5GMY41mudF1b/XYe651KC4F+s5Bo9FoNBrNisWSEBBHTRAJVo+dBuAV++G2dAnfBc5QSDAamuaplZr5pvYmQZaKGLLIzt7wtWVkyWYywOyFriSQj7QBgm1DUy/z+FqTrf0B+9pAqNAhbSIYd40ppYjHHNwJBXPyhdKZvzGNZoGRE1z85lm48heTMUuScOszRaXhEJNR8iIUULvuuZXP2L9ft4wtAyJyZrFUxBLE3Xx1HSXIFleWwDr+aTpB+NyzBWoK0VWj0Wg0C48WWDUajUaj0axYoj54IoKyRcXJ42QGsf0SGAZBqYRfLHDnK+HIxkYARmbaYA2RS3fiKqAvVO6cLZeeW2OXsVNsJWNEolimX3GZxkoB+fzMzrVx5OkTPF9eQpMLWQd6wrpW7Oj3afTDy+TnjTWwjgEei0an3IZSimg0ilkjsF5+OqDwmf8D7/m5dmstQz72nbKI9v3dcMfStmWxCSZUuTKM5X1eyliShJcj7A4JxeBABjTKZvJG2JFmSxibkHnc4JVmHa9gxOLEvRqBVQrGSitLYGUqB+sSNEOj0Wg0WmDVaDQajUazgon4Ck9EsCi7CpXkwv4MFzzTjzINDqUCOEsBwUgkUY/04DfEMXyfyHs/AN33nXVbzYZGdu2dq7yrOROUqhdU84UpMggnDFFVgY8KfPqTrXWL7eiHhiJAKLSkXvYSrnSP8W97Hqoss3PAJGuE86VSxKIRrLIR8PLTAYmSopgfwRdCX2xrFo25DAVdiQ5WQynsIAekylMVrbKNTZkcMWmQGpNkU2bdeil/9k6WiQ5WSwmy+ZXlPJ9KTF0pAuvHPtzDm28NhfFnL1ratmg0Gs18oK/5NBqNRqPRrFiivsIX1YrPSilifolDTQpFgCxrq6Y6uyxGM1/EzBUAMLYub6fXc5m2ZAP7y6qCANaUvBmXB5DZLEEkwmissTJtRz8kXKrONyG4ZE0jraMpDOUhhT1pO0pJ4o6JF5kwT0DBdCqSkGZp6OntOaf1z7f8ukBOEFiXcZGrWuwgh2dWj6bN8gouyA8CsPaET+7iGqFYKlJy9kJPRiJZ52AFGMvmUUohVsiIg/DTrEaWOKYzpatVo9FoNAuPFlg1Go1Go9HML11ddO3sCZ/v6lxQUSLqwxXP5OiNNoX3l0oRdavOJSWgGDWRxsqqDK05M5RRL6jm5iCwqlKBTCRJYJigFEkX0lF4sj0UWgFsKbl0QzumGiHipynYbZO3EwREbRMj4mAon3F5VglBxo5pgXWJybrZSdPG5Scpn3vnhUBNjAhY3g7WZ2MuYBP1Bsk7qyvTXauJvLOauBtGuHz/8e/RIZrBNHFiKVLvfxdc0DTjtoVlkSjV57v6pSI5NyAZWT63yTOJ/EoolCHoWXMFz7ZsxJEev5bt5dLM5O+9RqPRaBaW5f2LqtFoNBqNRjMDUV9RLNtUQ31VEa2pHC0UJJ0kpjCn2cIiIAQIoeMBFhKj6lZTKEqejy/lDCuAKhYZiZbdq0IgDJOYWyRRqla72tG3n1RrC0aqEcMbIJABQVmUS0qLpLRY+wd/QsQyMBwHK6hxwwnI2lPntmo0i0ZXV/UBBBMOC3OZZ7CejPoErstFw8+WOzBCnn9SYKq1tOUF9PZSwEYphfJ9BJCMzk0gtUt5hKoK7cp1yS6zHNae3p5pndgKONq0jkOtm5HCoGhFeWDVJSsmJkCj0SwgXV2wpyd8aBaF5dM1p9FoNBqN5vygpwc2L457JuIrSlY1IuCRNXDZcYPE8KLsflau2nbLUjfhOYE0SmSrXwN+cOB7/J57Dano9F4C5ZYYjjWAGYrv4sW3cfE/fwrLe4QXHFmFHXisHxxA2DZGQyNJmSNnTBbqG5JxhBAIx8GWOSCBqUD5PpmjJ+b7rWrOEWkIIi2tmI6N77nIYhFjmgJm5wU9PXUvgwmuXdNcws6nuSCgMDzEyx9x6W3sYyS+rjKrZIX+8GIQMBqNs96ortQQnRznMRUb27fiBEVcKwmE54XlJrCmi2kAdh/bPWmeOnWSY+t21k3LFz1G9x2iuTC2KO07W3b1gikB0zyrCB+NRqNZbmiBVaPRaDQazfySzUKwSENvn36Gkrm58tIXio+/cSMf+8AjALz1dkXq+k6SnB/5iZqpCcwAQ0mkMBAIDAV5LyA1g8iiSkWGa/JXMS06/umfePD9l7E6O4BR8ogPZAAwUk1EglEuP13+XgdUhNnmpgQAwolgyaqD9XtbFF/YaPD0R5oZ+V5ndT/dZ/g9nFCc64w51/XPM9xUlP/aMf4qwqY9vyD+/FsnLzj+d9vZA7s6F6VtC41SasUVuQIoWOD6eVpyRycIrA0oBKOx9YDgifZw+gtufgXNsbkJrIYSXHMyR18yEUbJXO4xtswE1hkRgkw0RdINn3PzzfDAA/Q3tCxvgbWrC9JpkiUF4rkX1aHRaM5PtMCq0Wg0Go1m3gnKRTaeOMcCM7MRVQYlK4pd1gx8x0SI2YubaM4vfANMVUKKGBC6ovLuzDftQSFfjQgAhGmxqjnJj5oz/PmzPkKBIcNLZSOZwlR5EiWFqQjH5QYBmAbb1zaF69sO9rjAqsBUAmWErjh6erj6daEL7eBHmhl5zznERWjB9Jzw4g5QPUd4h/fBVAJrmayb5eACn8fmha6uUAyGSYJw16vD7564p4v/If+gbp5prYzbQRkERPxMWL9JCExhMhaxeXjDqwmMshOyTEPcwTLnJhyLf/wkkff+ObIclaCCgLHi8hJYzSmc8xAK5koIijWjOMYZSLVycd/RhW7aObGnQ/Gx7wMCdv2zPpdpNJqVz/LvstRoNBqNRrOyWCz3KhBVJp5ZdSr5KBCzFzjSnF/4Jpiymr1rqNkF1lwmS1GY4fc1CBCOTWsiFCoONsOBFiAZCqTCNMlakkPNo+RsCMpX0ELB2sZQ1BVOBDuoRmMYCJTZNH9vcpyenknDvmeja2dP+KgplvNcRVrhh3f7frj9AHDvvUvboHkk62bJutkwr3M8dzWdBqVAKYyex/Cpz1y1reV9O1g7dNxTGYpWQMGGrFk9vjt7IemGj7gvaG1IzHn7wnZ4vC3HwWbFwWYFgc9oYYV00skABLhTCKynmtqXoEEajUbz3GZldFlqNBqNRqNZfiyxk05JiSlidcU8AgFCeJgNoTNRmLqS8nOB//3Kz/LWz3yQUlk8MpUi707hQhsXJru6GPyTt9fNcqJRUjNUDh+0fS4dfQa4DkMCKC458UzNBhycoDokt1HaDJsOBnEgz7zQ1RVGcMDcRdaeHv5u3ygAhW/thjvmpykrEeVN3fmipGR5l3qaG5/6z1B0LESzsKE8sSYSwFACX9W80507Ma3lncHafUc3v/oLA0MqciLAkBlco5lAhOd7RNlQboQdHum4QVt7y5y3L2yHmJdm/IdESclQzkUphRDL41uRdJJTTle+TyAEnjHhvOX7DFtR3GyeydLr8kChsFMpzGgU6XmoIEAs9zxgjUajmQUtsGo0Go1Go5lXpG3RFgjGxMIOs1TFAlJEEEDegi3DcN1r/pQ/uHkrLxQvAOBgbw+dC9oKzbLAtusdrDLMYK2jp6cyVJqGHu4eK9bNbm1MzCioHIt5XFs8xdrcQQaSF9KRHWHXwKHKfGE7mKqIHXiMX2Jv9BJk823AMdyEw+X5BE19CuWWEE7knN7ymSJWyFDwhUR5JXBDd6JX1nJ6s72kSkXEGbqClxNd93bxsYO7USjEDOXjTSD4zn9BrDmc8Nhj2L9262I0cV7IGj6On6Zkhe0XhLpo0q1GBORMSWti7seWsG2iXpptQ+UJD/0S7+Lt5L2AhLPMjxnfx60ZwfFYh4K+x7jcABCk4w2sWrLGzYwvJHZ5hIBhWZQe/xXRXdcucas0mvOMnh6y28rxRCsh6uY8YJn/amg0Go1Go1lJvPrTt/DZTau4uRDe5UeHF84BpIoFfCNCbSmTSMTBqBHJOld3nlNxq1RgLmrkgebsEE4ES80cEaCE4OJSgmZpM+JLhl1VKVQF0NYU3uwnr72Rd5Xv82u/OwcSLgXhcEn/49x8+HEMILG2WmBNOKHY31DK4Imqg+6KbDuBfZKXjrYggDXSoNjzMLFrbpzbm6sV/jo7ufpN5Y4LkebhOayuBMRa2zBsG8uEID2K2dg0t32fZ6hSCRwHQb24rvK5JWrRAlL+3pQa47wkn6QoAp65ajv+iXqXoG0v/9vB57/VJFYMeNmpgLbiYbaMbiDuGRgKYl4otO7oD3OPb7j1NyuxHXNBOBFMVUASFskb16fHiv6yF1iV7+Ma0xTzEpBOLF+B1TNk3evS0z1aYNVoNCue5f2rodFoNBqNZkWxcUQykhC4psJQgh2ZCDI7hpFMzfu+ZCGPb0RoLMflWcIktsxviDULg7BtTFmqvDYVFCYIrMWmGJtlFIDANvm3fT/lKukgCIWZttYwVmI6Qd434IcNw7zEa6Q/6RAEPqu+8Z1qG5xwMO4FIxmebQkFVimgENlOoWU/gnB4ulQK98BTcxdYa1Aobs630BY49FolZLGAEZ1ZTCqlogRRwXe2hK//5R9v56N/sfuM930+oLzwZJG3W/jppp3knCQX5vrZkBkllp06TuRT/xnwlleGDqCue7vOqcNmsfEjFsWGGHEFcWVyRa+PP6Fg0lyLQS01hajJwx0BrxpKo4IfcVlfCwUxiBPkecUzSfav2gTC4cpL2nHOJFfWcTCVIOrmyZaH4ivXJVtaXoWupsT36jLINw9DkE7zRDscXQf/uinFA0vYvJnwe08ynl8wLmorz0PY0wjGGo1GswLQdyEajUaj0WjmjeaCpDmncAIYv23yTh4lcvHl874vVcwTGNWhoEIqovbKEAs084twIpOKXOVK1bxNpRRuMgIUOdh6MYPJ7ZjjmYuA8APaUtEZ99G5upP0kZ9y7SuHsBTEkk2MpBqqbbDD76IkFOq2DIfTAzPgdFMbnnGaNzxGGBR59F7U635/zmKCgtCRZgeUkhs5ZkRxgjEyD/+cppteMP16gU+pMQY1js0LR+S0y5+PbCpE2J6LMWL5yHwOBZxsuhIxFn52zzZdyKPHRnnf66uC2sqRUGemGDXY33YRz3RsxQ6KXOAOEkwUWJd5BitUM0hH/DDLOBJkuXA0yxOrwmJlDaUsV514nIgdJbmu44y2bSRSuELS4BbIOknG8qOkP///kfvwh+b9fcw3KqiPCKjlQi+OJ9cg8zmM+NyLfi0kXTt7AOj5cDP7ZRyAwXgrpxrWEPg2lxRymHbT0jVQc24scS6/RrMc0AKrRqPRaDSas2b8homys8vxAepDAFVuYQpNqUKBF59I8WRrORKgoZGYvfzFAs38I2wHS1UdrJuPZSn80+eR770fo7sbORY6EPN2CyOJ7SQ9KNRcBVueT3NsZrGz+45uuvY0k5Lh97m2ujmEDtbfftQla/VztAX2t44Lo4r7NzwPz3yYz121Ad+IknCH+av0KJG22QfwvuTXc1xXaKY5iNCSeB4DDVUh8AtPD/P8DUPs2tBSnx9bvtEN9j4JTRG8uE1/cgslK8Vq0w+LOhnnf2dEkB7lFYNN5VcRig/tJq8MSlZVGA9UwOODRZSwEGoFuBbnSjrN375iB63ulRQtKFopnpaCG8SEiIAV4mAFGDMld/Qo/u3y0FGuJiTQGPkCRrJh6pWnQRgGGcPnAq9QmRYoOXWRvGWG8jw8OX3obkq0kd/9I5K3vWoRWzUzWTeLn1coFWMk1sQ/XXUTUhh8dd/j3L1vF9uu3rXUTdRoziv2tYbniIKri74uBlpg1Wg0Go1GM2+IkycBOJV0yEXWYJJGloqzrHV2yFKRnFMdHi2AZLkK/LIZxqtdHIuCsJ06ByuEmasF0yEBBMODAPSnLgPqxVWAtemhOQ2V7v74yPRtKEcENBdGWT02AGuq4mnWidPbeAOxsmaTd1bx8wO93DoHgfUiL84f7TH5+YYrOCp8FBDzw/fws8M/5Rtfe5zP/N572NQ2waXW00NgKZR0eHTddQwnwn0dLEqGBoZo61iu6Yzzh3/yaN1r5ZVIRxvYNgT7WssThaIlCFB2B8I7VV14PPs2pgjPLsufz7yogWYrTtYIuPwbOUYSlxAFAiM0ThftdlxTVfrABCsnIgAgGUniyPHSVvUIBZGCnLFQ3XR8cLfFt7bXOOBNk5y7ArK3Z3CwApSsFF5/75IU1Run696ws+djB3fDZsAMaPHCc+WB1q0oUf3+PX5ihG1XL0UrNZrzl29/Aa7oAyV8+EmXvi5dYFbOL6pGo9FoNJpljVKKSNFjNNrA0daXMZB6HqcaX8gv+kuzr3w2+ysVyTnxummpiO47fk5iWQhcaoUXhaJghTfy8s1v4mi0lW3D7ezoD+fH/FBgMosuu47uPecmiEiM1RmFAF54cDeOPzbj8vtPp+fkktvsxlHK4kTT+knzlFK05yT7+jJTristg8dXr6I/URVTlTB48sjArPs9HwhGBnFMB8d0aB9T3Pu9j3A60Vi3jEBAIDGtNlLxBlpjjbzqM7cAsCeZneSSnA+67u2qPOYLw3a4uJTAVIK4sii1NTKQbCaoudtTwpiUwbqSHKwApfQoSlSP9OLoCM6JASLH+nDy7llt0yp6xGocrMIwyOUXpmNwPlGehzvBkZwohecgKWDTiMGhT/498iW3LU0Da9gyEEAQEPMUrb6FZ5gcn3BO6816lPwVIGxrNCsIUfl/ZXQUrnRW1i+qRqPRaDSa5UNPD9lCmmwhTU9vD/geBoLH11xacaUoFE+PStIFb+ZtnQWqVCRrVwVWASSjWmB9LiKEwDPkJBdr3gwF1kAoetZeWpmedOHiQegYPcRri/1sOPz0ubfBNGnoTaMUSEOxbvSRuvkpT1CwIOcICjELN5/nQP/sQ/aiyqA/1UZQFlJSLmwbDKunxzyI+nDi+Gn8YHK2qrJMnm3eOKGhgoP9GXz5HMhiFfW3Oi/Z69KfbCNZo8MpFCrw2e6vYns+ztZCjAufPMqtrx7lT17sU7TgQLPiQLNi97HlWxzMTlTPhQqDJzrakaJ6PjQRlSzTWhxzZd10+4UCp+UYRsHltkfGuPxoiSYrSUOkAUamd5jPhHmql2iNwAqQG8vPR3MXFBX45GpynPe3hs72Wn/vD7a8iGOJ5sVv3AykApN0tGXSdOV7nE4vf2Fbo1kpSEMQb2jCXdeGHY+jenrqs3I1846+C9FoNBqNRnN2ZLPEvBrHYLFISUBvqp2g5p79l8ce4PrB67hy/fze5PmlEgU7AmVhSShB0tGXNs9VXENhyhKBESNWClCjg+QPPQvAsBNjNNZY9m8ofAOeit3PL17VxO/84XfnrQ2RdJ5Cf4FI1iLuwYsODzMcawF8dvYKHl0NhVgolKpSkYHc7I47V0gGEvViRNzLc/FQDIVADKcpbR5gX3+W7Wvq8ycD02AguYoDlTzYsCOiVCxxKl1kQ3O9A/x8QxVD0cwNQndzoCh/HuHfYfzsJcfS2KIaN+KUfFqkw4DpIgyDl440ElMGD7bWi3ALTa3DdbbYE2GF576cs4ZTjc/n5z4ENU5PzKnzqVeCg7U+7/inPBHNUhwO3cUCAZ2d06w5N4RpERVBXRJENpdHKXVWkQOLhu/hWVNHBBiq+nZ+svoyfs+XONbSf9Yf/y585jbBULx6PbBtCBA+DP+Cvq7ruah1eRTl0mhWOm4yihMF5UOpzcbOBjhL3ajzHH0XotFoNBqN5qyQhmCd08wq38bq95ClInm7BSuov+EzFBwezM27wJotlFAThjyltIP1OYsrFKYK3U9PrFIEhsHe1hTuHc38bvKlleVMCdsPDfKCXz3B1Z+ZZxenabLrlI8SLgroGBuge2MLO3sBpcjbIGU4BFZ5LoNjM7u1lOfiKIPBGoH1qgMFmsae4f4d61k3tgqlFPKB+3l481Y2tyWI1hR6G0k24ZrV2ymBwDRMlOsynHPPf4HVqxewS1YEz7Sr4mrN6UOKakalYzo0BTYDhkuisZnNpfBv+GsDNrJYxIhGF77xZ4ApFcIwGUxspr24g9UFKsrqYx2AgKboZPeqUCtDYK0Vl/d8uBkYrbwuRE1S85ApGC3lOVT+iZIGtBYKFLyA+DLutFO+j2dUf28VEJ8idaRkWhwdzrO1ffJ3YLFQQtAROAjDJSINBsuu2q1D1TarIEt/ZnE7MTSa8xk/agHVk0KhNaEF1gVm+f+iajQajUajWZaUGmNcVIqSCEw6MzG8w/vJRjuqBYTK4kWhmGU07yLV9NWOz4axCXl7EekTsaZ2aWnOf0qGwpThzXkgwqGypeh6Li29ioetVlAqfABNYycoRhfuuyIU9NolLkifYstweCiMa3lX9Cqu6FWwezfpz34O94UvmnY7ciyDQjBY4/YyPJ9r959i1VhNrEE2S76vl8dOpiuTFNDX0ErShStPw/NOw5X5RnZmk+C6jOTnP7Zj2eH7vOfG97CucSOnm67ivy552fSLGvVFgNqkw3Y3RVK0Mhy/nEx0MyYOfu+JhW71GfPRjz2FIVP0NVw+7TI7O3ays2Nn3TRLSUxjGTs0p8E0LJSAYtQidc2N87LNS//h8yihkOW/hyoWyBRnz0heUvzJRa4ai5PzmBWKU+mlES57ens4dOJxgnXtPL/QTGxVO2v9CCOxximXH0jn5/1aQaN5rhLU1iXQh9WisHy75DQajUaj0SwvanOburtxm5KMX7EJYVLc/xSe1QwTalqlnCSB75Mt+TREp694fCYoKcl69cUwkt4CFNMKgooop1neeIYi4dXnJhbtJrIRQFHJ3Uy68NKvfp8LWxZgGGqy6hD7/haD9/9yhG2D+znVsA2A9aPHaChdEGYUSx+Foj/awAXTbE7mshStJFLZGBIMKRFAx2g/+WCMTPIEw/ELEPgYP/siiaa3cdWGZkxAmYL+ZFtlWz2rQRqhAPs8t8Ro4ewKAq0kVBAKZP32RYzEmmjLT38sl6wUudTllOxWEu4Il/pP0mLF+e7FL2A0HorxgbGTY8d72XLRlkVp/1wxTItnW9ajEBxsCTN697eWZ4rpi5vYcuUWFPpvr7JIXnvjrNEJc8VqbMJzinhmCggLSGWKHqsblpdbuRbl+3gTBNZNQ0f5xbpt+GakUuAs7Y0xNIc4koUg62bZNZpCGYLeJHh2+G3MOzGSLmTLdron2kGIgMOP/iuv3PWXtCUjM25Xs/zo2lzNqNZ16pcXCjjeuAZhmGxHoa0IC4cWWDUajUaj0ZwxyvfBCAfaZqIXsWFsO//2X08w1t5Oe27q5fNuMH8Cq1siJ+sH4qQ8XRzjuUzJUDTK+qJR24YAVT9s1pGSdU0xFppTUZ9of57tkR6uFMcwskW+cG2JiJ9kON5C3IOvNZ3mc/FjHPxIMyPvmVygR+bGyDstBAHEfDB9RbKYJ+EWOWEotueeIR27gKwTOmR/uPtL3HLFn3NRTw+BV6Q3tSrckKhNHAUlA0bGCss/Y/IcUV7o0h2y10IhN62D57G+x9ictgjENg62gGu38uTazThBnouHTAIBybLh9y/+84t8/IrnsbZx4b9Dc8WwTI42NdPZBz0d1el2MEZT/gmGUtfCY49BNgt++WAQAkcuc4fmFOzaO8LDC7BdIxrDlvmqwBr4y97BqnwPz7TrvtYRv8Rlfd8hHV0LxnUk3FC4HP3fn0L9n7sX9XjvureLQAasK1Ylh+9ugRc8G+HBi8wpZX9LQn+2pAVWjeYcUTWFLD9885WsKlwEwGkv4CVL1KbnAlpg1Wg0Go1Gc8YEo0MAuGaKvoZOsjGDghWKQPtbqwVkdvaCEGnUqX8jv/M987Z/VSpSVPW3Z4ngOTDkWTMtrqGwg1BgNRXEPKYU1LYaHpaxQClZdcV2erAzeYqDeS4YHgDLIh3v4OLTAwzHw0zV1Z6DJTZyLP34lJuT2Qx5u4VIEEYeOAVJ+1h47PVaJVLBaeLuMFmnBQVEAnjm7e/mwmyWYxs30pcoi4CCytDncdxCkZwbkIycx7cDvkdRCgIxuWOnYFf/JhMHK28bgkPNAtesupzHo08UBq//4p/S/bZPn3Wzsg/+tPrijumX6+ntmdP2XOkxFklhEH73AX5vzy9YnznF6nf9T35RTPCVvb9icyEgJsMcYgB7BQqsC4WIxrCDHJcPll29w78kc+O1S9uo2Qh8PLN6/JoK7LJrO+6OUCybb5VSlAxj0Y/3j71vNzf/nuIdDzk8su4yTjc2MpKwWZcdJjDC72HSDd31oZNVcfOjgwz84t3Qvx/mIVtXs8RMGPmkWTzGM8hLpsNI/CJWlVNCDqdWkS355/dv/xKiM1g1Go1Go9GcMTI9CkA2Uh7uXGbLMGwbFpWb/HGUgII3f8NRValI8b77QzeWUgiliP3th+dt+5qVhysUVhDap3cMTB4ULQ1oGhvi+ns/uSjt6VzdSSFmsaNfkJJhVuTIZZtoKgwQlBtXtAVJu4Obil2TCjIByOwYO4ZXkfQESU8gFLR7OTBNuo/cwgEjQ6rwLIEIc2fTxTTPeoLhizp4euOldduyZAmhqseg8kqMFs7fTgmlVOhCVOFgSFUbhEso7CjCPFw/M4pSCkNSJ8pnnfBRez4TQKTYQiCXT3SIoRwMqkPZlVLEcyO4xQKRS3dyqRPeWe9vUeStsphlmji//utL1OLlh4jGsGV1+IUC0rkFiJ05Q1QQcNNQnN890cgLBhMVVzaEDu3aInYATrmj0ZIFrBoBXQkYzS9+TMB3vgAnmzvZ27GNvNNBwWnhcOvkiI2YFz6U6/FIdmDR26mZB5LJ6kOz9Pg+FEvknJoCs+XfwZElOBc8V9ACq0aj0Wg0mrnR00PX5t10bd7NH33jTgBKdluYbTmN1nCoBQ42K36Y6p9fgbVYpFRTPRkpidk6Veq5TMmQGAR1ImLcD7+DO04/zW/c/2Ve/aN/IxaZn5iKs2EwIWjNDWJLPxTzxrG38vQvfjVp+dJYlrQdrxToElLRnhkKb2C7u8GyyLqHEFQ35kVsDrRv5HjjmrptdRSOcuWpDJuHFdmHH0C5LunzWGAdz08eCUyybnbKRUw5jYA2i3Ya8U0GsksvvgEE6RGyiXoPrizkME8foTQ6itHQRKMhsd0sW4fCEQb7WuGxLUkcR9eTHseIxrBkfYZzJrv0Fe29DWu5+RhcOOBxcdbBPbi3Mk9OKHIlDYEdePzOE2GnQLI0VpmnUBwZrn9/C82ubBLLiXCqITwXTdXphRAk3EEgbLMViZBvaKO4UKMMNJrnCKocB5OJNtRNl6hlH3+yktFnLo1Go9FoNHOmZ1VAz6qAYwMHUY5DyWqum6+AQChiXhgPYJaHo0alQcmXU2/0LJBvuoNipiqaCKmILoTAapphfqUQenjbMsc1FFJJIl4feUtx5YCFIeGkVWDXL39KamBw0S98D25IUoiaFUdPOiowleR48qlJyz56eLJrqz9TqKuxZgY+bdlqVmv3oRsZI0vM7a1b76H1OylZFjE/jO2IuwH+OpeEOxYeoypAuaXz2sWifA8+8hH6v/ptrjjhY0oQE4TT1WN9U+ZAjmf27ugP/x//O44/7IDZxemurupjAQn6e8k6VceYLxRmKU3BVpiNTYiyiBr10pPWjcaXbwGnRcd2MGQOQyoMqSCbpVAsUZzHjsGzoTChGF/xsYcqzwPfw8BACIEQgiONiobRLLF0gTvvy7E2U/1+B0LRv8idAoFtEm1ZRcGpz1NNuOF1wXgn00sOnMSqyYs0bIf9qTY0K4xdndWHZukpx4XU/j6gQKXT5FwtsC4UWmDVaDQajUZzxkSkIB1JEg9C98y2oVCMMBUYCrYOQ0euv7K8jUHJn78bVX//XooThkYuiMAKochqanfscuendz7IHY8JfuOpPgDGzPAGwis9hTl+877EBZ1+8sb7MPIlGgqHaM8O0tkLcQ8SHvzkyMPkSjXDfwOfgZKsa3NrZghL1XRUdHfzuR/EaCycorMPruhV1SJGNawZOk0m5rO3LVNxlSv3/I4IwPNQQF+yddpFkqX+SdMKdtXAqsoPoSSOX+3QsQKWzd9OjqUp2DHyFuQtyJgBn75R8pq3NrJr7wiiLG7FvFEgfH8FG9KFUZz48inUtdQIIehoaMCWAeMBI8r3l96pPPGUFVR/R0tuUP19Mk2ypqTh5ACx3lHsXJHH2gfIlz9vgJGRMZRavGgLNxGhYMdnXsg0iftjRPzhuskHGsoCa3Nz9aHRaObMuIM1G6mPbJCWQaG4PH6/zkd0sq1Go9FoNJo5880vKt7xUlhfLLC3KTXtcslSjoNNAwSiHQidY6Xi/N2oehGHklUVWIVh6oiA5zgiGmPNmEJxhLzTSH+ynVWZ08iXxnn2R00A7FrduejtOrghGe637IB28zkEEW45vJv+5KvL+Z4CFJweHGPLurAAlioVGSh3YIyLrB1rVoFV/x6My3cg1CmeCA81rjsRVuKuzC+6bHx2H9+KtNHqj1WKNT18+H6atl2yMG96GaB8j5wdI2fHgPBGM+ZX9SrHz5EoDSDtsICVNMLOIYCOsQGebF9V2VbT6P2Y9mqON10culqHRxj9i/fDF/9xUd9TLV33hs7Ya4953KKaK+/rqn6Ld73tc9y0ORSoRCwUuGITHKxKQKptevH5uYhrKXobRsk7rZgEXOV79I+VWN88i0i4wNz5dARcFzashkP3wuv/MGyv59flXQZiCMcPXemG6xPxs+wvnxcEo1wzOryoha7cVIT8LAJrpFSifWyI5vwRTje2sa9cJPOwUrzS81m6QBfNmfKxD/dUX8xQvE+zSEzlYC2Ty+YmTdPMD1pg1Wg0Go1GMycUEG9o4nWZGAJBxlkTZkNOYQrc2reX04n6ywy3UJyXdshSkUwsgm+IyhBDs6mFqK0H5jyXMVvbEYHEVLDr1B4A3LEs970xA29c/PZ039EN90weHi5dlyfUALHBCGszfTy+uiM8jrJZeofSVYH15S9naMs15WOsLLAWM5O2J7rvY+hd6zGUi2dMyNSUEqPgsu2xHka+cBsX+9VMxmIxSy5XoOQHRKzzr3NC+V7FvVp7ilLl1y2Fg1iywFQhCb+2735ecNCmP+njFnK86/lZbvBrblKFYNhZ2uH12V/uDpsy1kLOuaUyXSlZJ6CJaOhSjZVGcGu+Hk/H81g6g7WOkiWIeSPknfB7o3yfwSUsdKVqhs0jFfTWR4GUvHq3ekCAWXa4G1LRlyhgKA9phLZsVSgwmncXtXp43g4jDsbd4BPZ2ncESkVSxdOcbhw/OkEJk76BUS5YrIZq5p+uLujpCZ93di5lS56TKN8nEAYFe/JIhUJ+fq7HNZPRdyIajUaj0WjmRKkhhhONEQkAYpxq6GBnnwjdckqyvfcJnnfiV9x8+KdcOHYCX9XfmBYL83OjKscyFK36TDeBIHoeikSauSMsC7t/FMtTtOWgvS/PFeuuXNxGdHdXHzOQMXyeNXO0ZgcYc2DMgbTp0zdSHYb+y6Gn6C8VCIwwP1EYBm2lqV0n/Y5PxBuZNF0EisZ8msaYzbf/oBtD5pBCEgjwpY8s5JfNUPd5x/fpS9Q7NJvyo+w6+SRdBx7ktH2Alx0UGCrMi066kCyBUDlcE4Tw+LfLFX9wu+T+CwJ8OVrZjiEVmXSWQC7ecOs6urqIFX1iRZ83PA45Kxa234W2WBtJp0ZgLUcEOEEeS1Xl5KytWNOgM1hrKVpw28FRtg7B5iGF8j3ShaXLKlSlUAS591KXkqkoBeHnp6REKUVpQj5sIOp/Yx9ozpJwx7h4MIzxUfd1L9rxrpQCBXknTt4uF7QCUsXTXDR8HEv6rEv3cvWxJxjzckhKlRiLcfpHJncoaTSauaECn6I1dSdafp6uxzWT0Q5WjUaj0Wg0s6KUotQYRRHe8GWiq2ktidBVAwTFHGtOPUWDYaECH08qAlXtIRdAfp4crKqYrxNYhZREAx/TWNp8Tc3SYxRKvHVXL3//fYFobKR1yhJGS8u7/+YWTED8vId1hTGSLkgBhZjF0FiBQCqMF3ZhJeuLvIiSR8qb+hgaiARsyQyQjXRUphUsQAVszz+NKDtg97cbWDKHZ6YQCFQhz2jeoyN1/gltyvfJTMie23T6EFceforISJax61oBk0v6n+GZVdWohKb80+UNwHt+JvmbvQO84Esv5dnhA6iySLS/BX7RFvCl/3ER933s6CK9oylQoAyLnFMehm1ZiP/ndaSiNQJr+bMXKC7v3cvetp2YpsWFzSlaE9rBWkvJFCTcbKXIGT/6EZl1G5BdXVVX0iIWO1TFQuV51olzYNVmUkpyVamEbVm4Cti5s7KMP3Kobv1RKyDiZ4GyK14sXnawKhVBwFgkrGCetyHmQcTPcMPRp7CC0OkVdX180+O2n/XzxZePknWqWavDozm6XlvtdNJlJlcYPT2Qzc66mGaB8H1cyyHpgqXgQGvoIrcIuKbkopSq/D5o5o9FFVhlubfN1IUiNBqNRqNZUahCHlwXEQGFYDC5ldaaDvD20WN46SF+96WhmNp4/a20P9FHtDLCUVAolvClxDLObQCNKhRQZoSCVc5UlIpYcP5WQ9fMnf1tgpyjeMsrFKnrO8Nh+suMSpvu6WLYVkhDUIiG18Z+yWUo59JoSIZjLcT9aqXtZH6svsBVDWsuuIzU8WcZiW8m68RIlQ+HHcd+RVu2WsjpM2/4Cn/wr3+HZ6ZQqPPawap8r869UxQBfW4v/tgY0oRRy+dLV6V4zZ4DpKONFO0WLho+xYPOEzhpG2UYxIZzCCdC9x3dvOvkjRwfLaKIggBLCQJ781m1LS4NEtJixDy7v71CcambZLUfZTTehG9U762EbdMQneoWT3Co6TCX9A3xmtd8kK2/8f/om+sJlCyBE+QxJDy2GhTD/Gz3h/m/nfsQKhQ7F/OMotzwR1Yi6N58A5loCtOwyO3v54XbVlFS9Z+fL1yykXBaU7SRSy/cSeT0GPvKRm7BKOvT+cVpe1kczkQb2F/e/56GAq8YTZfbEmJefBnJ3sdQ+VG69g/x9c6Nle/lUFYPY15JZN0JYmo2y562cm9Fbw+7Fr9Jz2mU7+Pa4W/gzl7oWR1OD4Qi8H3cQJ6X8UBLzYILrAcOHODrX/86zzzzDNlyD0ZHRwcveMELuP3227HtydHVhUKBr33ta/ziF79gaGiI5uZmbrnlFl7zmtdMufy50tvby7//+7/z+OOPk8/nWbduHa94xSu46aabZlzv8OHD/Md//Ad79+7FdV02btzIa1/7Wjp1xohGo9FozjNkPvwN90WUX62/Gc9MVPLUdvZChxvetP3D98Kh2jd9pps//ZtbGR6sbkP5AXk3oCF6bgKrLDtYD7XA5mGQtkU0WCCR6MYbF2a7mgXBNEz+z3+Fz3d9ZhmIqzO53bq7iT20G/8//hkIC8Yp36N/rEjs5FGO7LihsqhQigsak1CYelMlW/C7j3v8y86fsmZsJzk7wW3HjnHF//0yzsatleWM5jYSFPGM8BZAFQuMFs7Tzgnfo2RWne7bR2xSxRIHW0PxJriyk56n9/EnaUi5PwfCfNwfXOtxSBRAwSXCYnwLf/3yv+e9n/yfmHITKBCGwYnkmjN2AXknj/KazGoMoCAkQXoUs7HpjN5a8cgBLl0Vfmf6k+1185pTceJO/S1e9Hk3cMfd423MEPvrXRhaXJ3EaEzgBCUEAWAiEDR5JsqII4JpDr4FRPnh71reakGZKZ5oB4HP1+/7HFet/RNKqv631DdKbPxgIwAj7xkh/8+3Thp2PzAyNi8dnbO2vZCnZNo4MoEpFZ4JubjFE9YJRBBgeOAMZzDWCd753k7+9i/uI1kYIevAeFrrSMEjaypg6o4lzfJDCBHGQ3R1QRDMvoJm4Qh8Sma1kzEYP+ULIAgYK/pEklpgnW8WVGC97777+PSnP42UEsdx2Lp1K5lMhr6+Pr70pS/R09PD+9//fiyr2oxcLscHP/hBjh4Nh9sIIRgYGOCrX/0q+/fv5y/+4i8w5vEH4dlnn+WDH/wghUKhsr8jR47wiU98gr6+Pn7jN35jyvV6enr4yEc+QlA+cQgh2LdvHx/60Id429vexq233jpvbdRoNBqNZqlR+TD78ck1O/DMZFhJG8LiO6bJ9bFVnJLl3/NyxVLfCMjbAUpYmIYJgU+u5NMQPbfOUlUokK/LYFULJ7BqNAuIiMeJ+qN4Zllg9TwG+gZxEk0Mx1pIuOG9kJ0tstEYm3FbH7/Rojmb59pjoVh4YSmK2bqqbhmzsRknqG5HeS4jY4svHC0GgedRsmrPNQpnQsTCz5sL2L0FgtVxAs/Fz+dBTH3DacSTtOQPg9hEwQYLizfsSzL00pfT5hamFtN7eiCdBtsOq72PjFB6cg9v2lu96S09+QjxG1445/elPA83GaEvEb6XiGqvFEETySTrpqh472y5BOkq/IiFXfCwN2ya8/6eS4xEBQKIu3myTnhMbs459BaAie68RUB54e9awW4kVtMPYiq47tMv4paR9aSjm7lh/Q0gBIFy6yq6KUOAN0ChfBgIy8IvFckWfZri1e9g173VYnzz5fqX2TGGY6HY29knMBobufWmN/PW0j04x8I34yvC46a8/1QuzJGW5bifwPP46H1NXFZURHxF0N6GuX3HosY0aOZGkB6hobkNwzT5p4sLJB/uYaeqZlRPcrdqFhzl+5SmyWBVUjKcd2lLRibP7KopzqmPtTNmwQTWI0eO8NnPfhYpJbfffjuve93riEbDfKcf/OAHfO5zn2Pv3r1885vf5Nd//dcr6/3DP/wDR48eJRKJ8OY3v5kbb7yRdDrNpz71KR5//HG+/e1v88pXvnJe2pjL5fjQhz5EoVCgo6ODP/qjP2Lbtm0cPnyYv//7v+ff//3f2blzJ1u3bq1br7e3l49+9KMEQcCmTZt429vexvr163niiSf4+Mc/zuc+9zkuu+wy2tvbp9mzRqPRaDQrC1nIEwiDTHQdKRdMWb6PUwon8Gj9z/+g46UvrVvHN8GShYp4hJTk3HN3NKi/eC9jkTZ2lgsqW43NNORPnfN2NecByeTsyywjjFgCJ0gD68MJnkd//zDpji0ky4KKkJLG//d/svF56+EFL5h2W9G1F8L+w9gyrMRtuj5mQ1P9/hqaMGX9jW56NDsnR9tCiDDzycT2FUseqqw2HWoVjLU3cfXuYt0yXfd2YRbuozQ6e8EPEU8Q9TPY5IFQxDQtmyPJFG3DcxOpVRAQDPaRtWN89oom4u4osW+8n7c/vwsxRwNJMNgHXvjlKFotDCZW0VhuvlCwrmlynq6wLGIjNUPDdVzblASmQChFwisw7iqXMqBkxfjHb5SHOnd1LZ7oUBZYi3YjeRk60ATh769DHCmq4okwTDa2rMeKVDsbu+/oJvfXG/jT9hK+ESFnhbED6QkCK3t6qs/vmJ+my3yWkWhDtX1K0BxzmM6Nev1bLW4c9VhFEShXPfd83Pb1yME+/ADypk2yclRrlhOlp3owyueVdV4U0/HxElFiHY0gBGJMFyxbdAIfNxqvdMCFQy/G50nSM8UD9fSE/y/m+e48YcHGBnzhC1/A931e+cpX8oY3vKEirgLcdtttXHPNNQDcf//9lel79uyhp/xhjrtALcuitbWVd77znSQSCb7yla9UogbOla9//euMjo4SiUR43/vexyWXXIJhGGzZsoU//uM/RinFPffcM2m9L33pS5RKJZqbm3nf+97HhRdeiGEY7Ny5kze96U2USiW++MUvzksbNRqNRqNZDii3xGg0RSCqlw4FC7Astt52PabjTKqg/vFXfApThsJDIANUEJArnXtFZglkoqmaKYIG7/x04WnOb4x4gkiQrrxWnsvAwAjHGtZUlxEmO9Y2zjqk+9lmk4IZ0JKHlmxA9MTApGWEaZI30kSLPtGiz6MHfkZQzJNZwkrpC0XRrb95lAIiBJgNjZgNjVOuIyyLzq03Isr/Uk5VsDfiCQBa8kMEAoLyqfBH8UYKxsyC5a2/63Pzr6d55edeyEhg8sVLbuBI0y6ebu9iyFqLHEvPuH4tQXoEpCITvYiTzbeScAGlQClsKVnbEJvztjSTyWfSxN3635OIaJhm6YVF+R6sXk3Bqf++2hIsFeP6w3DFSR8eeAAMAyEmH8fGvf9CMeqSi1sEMuBH+77L7/7HH59dg7q6qo/Z2l7IM1r3O82sRdWKhsIOxjANE9MwCdLD5OLNBDh8+9IX8M87X8HPxwR+oCMDlhves/uxfIXlK4RSRBIJiu2NCMNACIGTaqg4ss+ZM/gePpdRvofb1hF2qNV0qm0bAnVfN2Pv/cvpV85mYXQU7rsvHIGhmTMLIrAWi0Ucx2Hz5s3TDrHfvDkMhR8eHq5M+8EPfgDAxo0buf766+uWj8fjvPCFL8R1XR599NFzbmMQBPz4xz8G4EUvehEdHR118y+99FK2bNnCgQMHGBysBshlMhl++ctfAvCqV72K5ASnxA033EBzczOPPPII3nydRDQajUajWWJUqcRobPJN5hX9+9mVklNmEArHwZIFVPkfgU92HhysUijGIonqfhQ0ulpg1cA739tZeawERDyBcAcJZEAgA3pOPULmJ/9Vt0z0jXdwcXtqmi1UKdmCe1f1c9gf5oA7gGVPdjICNLavwTMKFCzIWwpVLDAylxzWPT3Vxwrg//3J35A1A7Lm+DnHx5hQJKz7jm5MVTMhmaT7jm6uyjdyVb5e1DIS4TV/x1hfuE55vcBweHDVxinb8JpX5Blb3cSVxWaGGh16jvyc733z+7g1uXinnC2UhganXH8qZDZDOpZiMNlZydQbfwtXjBzDsRY2W/N8p+gWiLt5ki4k3TDXOSJSmNEoZjQa/pYtEqrsVPas+tiHqK+wiNZ9jzBNEJPvPUWqgZft7eXaIz4NRYU9NIr028KZzc3Q3EyQGSXrZvm7f09Xpp2reKVKRdKR6n2ygHrXbA3dd3STdJKISKQuwgQgHW3ke5fcRPfGJnpWw9/dejGPn5p7h4Rm6VA15yJhGGHnkGbRUL5PyZx6wPruxjQf2DhQN/JDMz8sSERANBrl3e9+94zLjIyEB1gsVu1lfeaZZwB4/vOfP+U6u3bt4pvf/CY9PT3cfPPN59TG48ePk8vlZtxfZ2cnBw8epKenhxe96EWVNkopp13PNE2uuOIKfvrTn7J3716uuOKKc2qnRqPRaDTLAVUqMhptACEqN5hrxk6zq/cZjMjUN03CcSoOVoCHjv+cbe5V59yWvGlXivRAeOPWqB2smhWIEU9gKA87GAujNLJZYsWqC00oxfatG6qi2SxD9SwJkezMw90zEYEdjOEb4TW4KhYZnWmoYJlsYWWJGkJaHNpYI5KKIq98S5LO1Z1AfTX4K/rC/61LOqffoO1wukHwokMncPwtFOwmDrWEs35lmOwYK9GequbZySce54ZVzezZcinp9m1st8ywiFkyUhV1BWS8IqfSBbbM8X39wZd+izubNqFEvZDaUMqyY+z4HLeimYruO7rZ8+FmTsmxULwuC9ib/VVEm5ppLUDh+EHiZzls9oxjNnyf4A134PfsBfxKlqoay2A6Dp5ZHxHw+ds/y5ZV9eYfI9nA2swpTqXCyDtHGSgZI+f6JACyWQwJsdL8FiSSbolMpH6kSVNseidc5+pO1sYD7HT9SNUTjeuIlU+JW4ZBKZNn//JurvyXj81rezULjyrmZ19IM38EPu4LXgxe+Txx4Kfh/wpU4FMSFk/19ixZ885XFrTI1XRIKXn44YcB2LFjBxA6Q8cFz23btk253oUXXgjAyZMnz7kNp0+fBsCyrIqbdrr9nTpVzXXr7Q0D39rb22lubp61nWcisA4NDc26TFNTE6bOTdJoNBrNYlDjYFEf+LNQYK0hVQwztay1G6ZcXTgRLFnNPDQkZM8xIkAFPqNOoiZTCiwpSfizZyieFTp7SrOACNvBNRQRtw83liQI/Dp/nFVyuXx9y6zbGRdrut7RjBAzC6FZR2CrLIER1gr40f7vctm1V571e1iWdHXBZT7EstVcXsOjs71zkrB1sN1iy0AoLs3kExZCcKQRTCW5tO/n9Kx7MeO3Uglpsr9/rE5gLTTHuXxoPT1rt1fiBAQRki4UJ9yBHR0tzllgveRkicFEC0mXcsX1kBfv343TsXaOW9FMx669I4zecmVdsShpxvnONnj5fgBJ5Jc/x2xuhpGFdeQpGVBUBoEM6tpDEODICJ5ZI1gaBpEp3MtGMkVbdpinV/kEwkIEPt7JY4zkXG5/dZqPfU8hy6sZUrEnVj5/9Paw6xzaXiiUKFpOpZK8UNAYs0k6SWA0nDZh5EvJFDTK3MwbFoL+aAKp1KyxKZrlhczpQleLifJ9Sqp6jOSc8Lk0FLYSYERZIjnwvGZJ/qLd3d0MDg4ihODlL385AGNj1eEAa9dOfXGQTCYxTZP+/v4p558J4/trb2+fVrBsbAx7vWv3N77emjVrplwHoKGhYdJ6c+Ftb3vbrMt86lOforW19Yy2q9FoNBrNuTKewUq5KmzUhwvSGbxcltSlU3cmCtvBqrlZMhQM51y8QGKbZzeMVRWLpJ364ZJNbl4XvdAAy7P40mykDY///ste7tu0maIV6iiK0L16cdQgGZnfy/V/+d2v8Sd3vY7xK29HCoZzC9RBsYS4UhDIgIKbDUWdKYZPA5BMcrCswe6apUMlc+lW8t/6Ff1GkVTxEIF1MQAWBv0jOSAceh1kRnENyeGWTVMOKD/YAgU7zHG1gOMZD6XUlFErtSiliCuLwUQLO/rh5xeE068/8iuSXh5jujNhV1eYqVd+v5qZufRvP0nxP/+t8jowHHxh80/Pu5iO/EaSm3p548MPEZ9hG/OClBRqBJKg5uONqkhdRIAwTSLW5HtaI9mAAKJeBi/SglACWxkM5avHw+MdAIqcAw3CASGmKUU1d0bzXkVcBTBf++s0RK2yg/y+Sct339FNMDrMe/6yi1rLUdITBCI8iva3hudGz07z20V/RkesZvFQcm7fFlUszr6QZv7wfdya84dpmFx+OiDphr/7KLCY2jDY9Xqfb34pfJ7SkcdnxKILrCMjI3zhC18AoKuri4suugigLq80Hp/+5yoejzM2NobnedjnELjr+6GDJpFITLvM+LyRmt7J8XbOtN54LuvIAvdqajQajUazWLjFItkaYdPIF2l79lmc4YFK8ZeJCCdCSmbpQ0H51j/wfYZyLqsbps6HnA1VLIQO1hrVosXVw840Kxc/GqF9bJBIEFAsCyRSKKLpDM/72ufnfX9GMsXaSIy+shgngNFMtirw1WYv1giOyWDljKDac3A3mc5bEZYikD47O3ay/fKtvPCmznParjQEpUKO++M5rnf7OB2/GIHANEyGRjIVV90ffO6V/EmjQTraQDQIT1eBoC7vdVwsC2RAzvUZyXu0zFIESJVKKBGlYNcXsmosDiMkmD/88Tm9P01IqiFBELg1MQyCweQ2RhLbaC3BcKqD+y6+lpctcDtUEDpYTcMEVT/6w5FxAqOmo9IwpszfFZEoKEXRzIAI3fDbvCQjeZdGaRFpbeRtvTYDtkeyWeEY4XewoKYW/a++dDcABz/SzMh7Jt/rdt3bBUpx/f4OOtSqyvSmRBRDiLAT7A+mvocX0TiWzIeOXeCxvsfYWZ6XdahEBUQ8yXC2qAXWZcJ4VvCsy7laYF1M1Ec+jHvhtWDHYefOSfMdJbCYwri3ezfqQlDjvb3B/MaHnO8sqsAqpeQTn/gE2WyW9vZ23vCGN1TmGeUfCCEEjjP9xYVlhU12XfecBNbx/c11X+e63lz41Kc+NesyTU1NZ7RNjUaj0Wjmg5GCXxmW39mrEH5A2+jM0TbCcTDwsYMsrpkiXUzz8Qc/ys3b/vasBVZZLJC2E1D+iRWGQfNHPgwXNJ3V9jSapWbICUh4cMXpZ7j/ou081gEDZoHsi8d425kKCLs6eSc9AHR/fOqOfuFE+IMrX8efPLanMs0rlsiWfFLRKfbX1YV68AE6fz1Bs7Q5GZmb23JJCQJiKk6urDc9cPwBrrpmx5SL7ipnstYxjUmi+45ufvYBwT98D75z0zCiEZRQBDLg64/9O6+9/mJaEg6pkmI01oQSBoXy3ZapqgLROOPiLEHAqXShTmANum7h99dbBAK+tSVUZlWpSNFKkig7inb0g+W6NKZHiBdATFPQRHNm2LEYcZWnZDaVpwQMJC/GVLCvFcDhPy9q4gUvfDFRWf5QFyJORgYUZFU0HRfoAwGmnNDhYRhEpxJYhUD6Pk6Qxisf3mYgGRoZ4+pSM8VEilONV1G0m3lk0yD90UewZRGw2b7/KSIXX16/wTmILZaE7fs8BstDkjFNVjXUdArceOOU64lIBCUCDFkkMCJceGSUQEDBCguO1RZ1GxodY9Oq2Yv/aRaBIDwG1nphREpEhRdoA4kWfrD1eYxFUkSky+tzJWLTbkQz3ygBpXwRTAmPPQYtsC6dZswJR1s3SRuLVVOue4mbpHRhEgSU+rNEplxKMxWLWmbyy1/+Mk8++SS2bfOnf/qndU7VSCT82Axj5iaNzy+Vzm0o07hAOlOe6fiFY61QOt7OM11vLrS2ts760PmrGo1Go1lsFIqRmsI7CEEqn8GYbViYZaMEOEGGpAsJV9EyWuIt33znWbdFFvKM1FYmlopm7WLRrGCMzdsQgeTy3n10HeomPvZLBrP/jmGeZVGpXZ3hYwbiqRSGrI4eU26JkRkKXRXaGtjox2mSDttVC0HvuddDWEjsVIrAiLKzF3b2wuWnA2Lx+bm1j/uChAt5kcOUoXv+il7F9Y8PM/jboXkk7im+sr2JqB/GqZSEpLmQ5s0P388lp/fz33/2C5KloygUvvRBSk6lq+4upRQfuvAIETvCm592+Pa3k6iuW1GlAq7VQMEKRSeUornvNJHTg1iXXj5Fa2sIAu1EmiNGJFpXzX7naVUpMDXOBX6UUWeBQwKkpPiFL4E/ObvclvUdHMIwp3SwAgSui+Nnqq9RDJ48TVxaPHjRNRScNpQw6Ut1sK/jZfQnL8E1k3iH99dvqKcnjAlSCsamydPc04Oz5wmyRr0k09w4/ejPynsQgubmNdjlaKFDLZC3qg7wcKHwv6GRWbJaNYuGqvl+fv4yxROrNvNE44X8eMsNPLI2xf5WeGKVw1dPBEg1VWiKZiFQgGvWn7jWpXvJOeHx1BY4bMtfgOvXX8v7EZtLvRTKECghKLalkEVdSHauLFo354MPPsjXv/51AN7ylrdMKiw1Pqw+CAIymUwlx3Qi2ez8hCPPZRj/eNEtVXMiONv1NBqNRqNZcfT0AKAMwZisv3ErijT728I7naumWV0IQclQ5Qrp4TRTCaSc/UZrKrru7WJzv4Fq2MHO8XtfpWYdVqvRLGcKjiB+YpAgGeN5faNc9/MC4pZb4dMLlydrNjRg+CN4Vnl44He+zeA9n2TDyLFJyyoUXirKHY8pMARsWI13/AjWmgsWrH1nQ0+5GrKhwE4kCYyaSBOpSDXMj9vNbGiEbJaMEWAHaXwz3I+vAgYjSbYB77vqnfzRD/6FXPnU9PzDHl9f/ywNR/eys+9xou0dRD2TbDQsjKt8n1OZQsUZrHJZGgKThsDEUS6rG9fi+xKKBYpWA1G3+r5aClnM2Zyr48IYVLNYNdMiIlFsWfN3EuNjZas0BhajdpTVxQxnSk9vTziUnplzo1UQULAme8fEFFm7EcectujTbW8w+M0jY3XT3P4+BhPbaC3VF9Er2HC05TKeNbazu7fEiwKJNV1m+jRxIl4pT9aOU7DCv5kQAY2J6JTLTuSPb343b//uZyla1XbF/dC9W5tBe3o0t/yd9M8Vyh03CjjafDUjiXae7oDGiYkAvk//WOmsRzBV2L2brt8tdxbd27Uis9cXA9ewUDXnCk8oLhw5ziPrtjLeU9HmOQydPMmaC9dXl0tN/HwEMjOKEdX+47mwKA7W/fv388lPfhKAV7/61dxyyy2TlkkkEsRi4Yc2XXGoYrFYca5Go+d2YLa1tc24L4DR0dFJ+zrb9TQajUajWWl0vTZL12uzvPqVBbIThiOafpZC1KQQnXlkRdGQ2EEGU4ajlDa4UURwFhdpXV0EP70P8fQJNvf7FReN43uk5rkIkEaz2BheQHQ4i5MpIM6hf777ju7KY8b9JRtIyDFMw8Q0TH6UGuCVO07ReN197CkLlePIKco0BYO9Z9/IBWZd0UICvjkhp3QODro5k0zyzj1RHL9quBCGQX80FHFlPksitRFTCUwlcAJFzhzBD3yUIRBSkSoNIcr/lO/h+pKcG5TXD80avY6LKxS92V6KZoDM59jV30zSEyQ9QeOLX8HaDetJXTP1kOs6LCt8TDM8W1NFOBHsYHYhetSJ0rWzJ3zc2zXr8gB33bWbjUfSsKdn9oWlDAXWsogY88LH+HErFJXzRdyZ/nfw0nU7eTo5ihWE99Fbh0DteZTexu2Tlq3dx/6ixaOHzuxYz7pZItKmYFWPPyUljbG5dYQa8QS//vQoSTe8ZqiVT7NO+RERFIqlyvGiWVpUOSIg++4/Z6xjGwhB0g0/O1OVH6aFCnz6xuanoKIq/9NMT8movz53DUXSzRH3ynULFMQKPqMnT1WWUYGPl4gihcm+tk081bGNdCSpC5SdAQt+R3LixAk+8pGP4Lou119/Pb/927897bKbNm3iqaee4vDhw2zZsmXS/P37w2EKsVhsxkJYc2H9+vU4jkOpVOLkyZOsW7du0jIHDhwAwqH744w7b0+fPk2hUKiIwlO1s3Y9jUaj0WhWGlkzvHlJKDN0sJZjaoRSvP9lASXKozpm2EZH0wUM9I+AFd4YgUHkdBpfSqxZYoFq6Xn251y3thUVu4BYzfV5SzatHSya8wJFWUywrIXJc6zBSDYQ9YdJE17XPq/forFlE1v61mDa9aKFmuLwUucY1bUQfPOzWa474lNqSfLwZRtQNT4S35uhI+Ys/9YyCIj5I4wHOQghGIwm8TyPUqFIyUhAea6QCoLByrpGIOnIFzkpFFJY4Pvc8827+cL//SGG18f337cXN3CRShIIQa+T5D/aT/OLz7+cFyRuxyi7UYXtsOrzn4Em7SyaT0Qkgi1zXH66eizEp0jQSDvh3/1j/5GF7/fAPV0zfp96ensIZIBSirHCKIcmdGZMQkoKdmjYGXdxjhMrBtVMX+kTm0FgBRixAraW0vhmOwJ4NDbKtqHwnJMcT7Urb79gg5QBj/U9RuLoxVy5ZfXUv9e7d1eedt3bhR0orszEsJOXgVfdnpKShujcZAcRi9OUH67krkIYhxH1s2QiMRRmKKx5HtmST1J3sC495YiAgcAi62anFz6DgNHCHCMUp3FHA3iBjykFgVCVkQuaepRSFCfEA3giwA58DjXlQZQ7HBWMZqqFYoP+XlQ8yommy8C6CIBnOi7ltZk87YvV+BXOgjpYjx8/zl133cXY2Bjbt2/nj/7oj2a8CbriiiuAME5gKp544gkANm7ceM5ts22bSy+9FIAHHnhgzvtra2tjzZo1BEHAL3/5y0nrSCl56qmngFAw1mg0Go1mxaJgWzHO3+1pYfRH5QvcZBLx4tu4uG0Dnas76ZyqQEwNriVwVL5uWquMMNo/c4GsiTipFK1BhDX5izBr4qJWjZ3ZdjSaZc8iOAyNZIqoP1x5LQApbE43PZ9n12wlY1XdZrJ8u3y0ZR2PbNjJod+5k1I+v+yisJJOEhQEtsPTHZdhqvL78n22RpqI2vNTx2DX6k52re5kZ/sOEuQqQ+8bS+CfOk3/xZcz/JVvVIfimyaWYfDzp7Zz1bZbaCpIxDP7GGl0sIJyrFjg4wlF3kqQNQNUschgVJK3kvxw60v42cZbOND+a6TUZTWV7cFwInSkdPmReceyMeTsGZ/p0QzZQpog8Mm6c4teSLjlQbuK6XNMyygZUBBWNd5hBuKRmbPIT0VcUqXp3ahbhqtu0ZgXtjNIj1IqFBjMhqKY6tzJRi/O1cUmNprNdetHPcXL97o0xK6nv6E+OChWzEwfMzABI54gVRqbNL0hGMSS1WsJ5XtkitPnRmsWD1WOCMiWo6TGr9Eq39qy/qMCn3T+zGrUTESWihTWtfLK/Gpuy7fznc8XQzG2a24O8ucMUuJOiI75zc7XEQ0EUb96fCkBA5lqvqrMjhEIg9H4Bp5ohyfa4VdrTPYN6MzjubJgAuvo6Ch33XUXo6OjbNiwgXe/+93Y9swn/ptuugnDMHj66ad59NFH6+ZlMhl+/OMfA3DllVfOSxvHowq++93vMjw8XDfvySefrDhYJ+5vfL2vfvWrFCfYpe+77z5GRkYwDIOdO3fOSzs1Go1Go1kKdpZSdJYacE0bz3DCnK1sFmyH//ydf5vTUOS/fOFdRA2Dq0+WSLpVR0p6aHjG9WpRgY/hRBiLriHn1Du1Lv7X/3PG70ujea5jJBuwVAm8NL70UYSutf7UGh5b08kXNl7NiVgTAFIoHl99MfdddA1Ptm5i9y8O8cVME0Mjk0WQpWT7mh0UNq3mZztuJB1tJO6BIcEUBuvzM/nsz5DubujuRnTfR8YuYQfVDE5lGvQ1tTHiJNmZTZIMTFJOkuZcBqtGJDMSofvfrhHxtpeSXOdfwjWldchCDgWcbL6Ngh0nUVIIJbCDrXVNWdUQw56jcKWZO0IIPMOjfSxHsqSmFTjTsRSf/JZi2xBcfnqWoo9nQzkiYGefqOtYBMhbYcfHeMsSzY0zbqqYiJIsnKBgQWBMdqbfcngPu07tYU3m8brpqlRkMBs61j0h2SSaSFpJ7npwM3s2b2fMsBgzA676ydPszTsMJWpGoZb3cd37Pjjnt2wkGzBQNBQOcnl/WCSusSR5++13EsPDMiwsw0J5HmOlycW/NEtAOSIgr6Y5F5kmjNexyZ5bsSTv2QME5biJsYRJJJGaFGujAQIfdzhdV9wwmgojbApmbeeqoD/vhx2mXV3It76FETNKIOo/y97suQnjzyUWzFP/rW99i3Q6HBZz8uRJ7rzzzmmX/cu//Esuu+wy2trauP7669m9ezcf//jHufPOO7n22ms5ceIEn/nMZ8hms8Tjcbom9FAMDAzwjne8A4A//MM/5Oabb55TG6+77jq+/OUv09fXx1133cVb3/pWNm3aRE9PD5/+9KcBuOSSSybFFbzoRS/iW9/6Fv39/dx999285S1voaOjgwcffJDPfe5zANxwww00NzdP2qdGo9FoNCsB5ftcUkqiUDy8/pryRAVBgOXYc849NVIN/N5DJfaszQFVp1V6ZO6FQWR6FCEgE13Hqpp+zfaxQTounBzxo9GsNAqx8HhKOckFjwcAEIkknqGIuafZOrqlLufwUAscJs3ndwgeUgrPEDzVWhb2aoSmbz12gjfccimmsfQRHcFYmsLACfZ2NPN4x3rGSxLtb4X+C5P82R9/aEH229S2nsLQMBCKW9Iy6G1sQ2QKYJfDA669jpZ3/nfYtqqynrAdspZEeGkCq4Psww9gNEAush4ZXc+3fnWIgVToAky6ULRg8zAgFEaN0LambWZRTXP2FE2FFWTIW/FweD5i0tDnku2gGpr55xvCYfyj5pN8wHMR9tR5o0LB5iDJr4/GKArJv7bM7GD1gwBvwjDfLcMQpAOeWBV2igAgJfHUzEXclICSyJEsHSfhrq8IrXk7zGbcNHSESKDY3wz9ySaykQ3hesViRcj09+/FuNDm8qEdPLZ2E1/bDkeNizGGv8aLizYHY1u5bDB0ve0vJ+UZ/ijv3D73UZ1GKvxOt+Se4pJ+SEdTXH76MG1b/wjrxzkYf8++x1hRC6zLAVWOCMhMyOoPjPA8nHOAssO7UHApesFZjygI/vhtAPQmIeckGWpMEM3tP+u2n68o36M04dwRb23FLLpcmB7mqVi1k6XghfnfyZ4eZHOc71zzGrYOhdcCAAhBtqTd4nNlwQTWI0eOVJ4HQUAQTB9CXTvE6Pd///c5fvw4R48e5ROf+ASf+MQnKvOEENx5552kJvyAKKXwvPBDl3LuvYeWZfGOd7yDv/qrv+LUqVN84AMfqJufSCR461vfOmm9hoYG3v72t/PRj36UAwcO8O53v7tu/qpVq3jDG94w53ZoNBqNRrPckLksjhJk7BR9Tgfxyn2MojURmbOoYjaFV2ipUpYtw+Fzf9QnnZt7YL4cS7PKi9CfWs1FNbrspc/uQ8xWPVujWeZ039EN71jcTnkhBINOwNb+A5jGRqQw2dEPB1sABUoo1gWrOJ0pUoyk8IzJx1mxWOLgQJaLO2YWdhYD//gR8FwOtO6YNM90DhKx5iceYCKZqCDiDwEbQYHvWBxqXEVSBBUxWtgOrYnJgtuIHZB0RwHY1+SDqgpm3z/5NNLYUJe5OS5YdfaBWb4zXtNRXwFeM38UDMXqbC+9Dasr07YMQ0/1JdIy8VrXMhKL09dwGaNRmzd99C0cWX2U+944uaPkkqyD3xwjKSEJdKUbZmxD3q/5AghBLm7ij/qTHLUqkDMWuQLoXN1J7uknWTX2CLC+bt4JcRKrUAQn7AS1gpohw7kxsvnw99qPOwTC5GjzRZX5F8pV0PSHjI3+gsCodqJuGYaOzGne+LOf4fx/cz/+jMZmZBBgAM87GbppW05nINmALavt0g7WZUQQ4CvoD6qCXq58ypMTLxUDn2zJP2uBdfybn4leRF/DLn5xocBs38mu4V6W/pdo+aB8H3eCwBpxLBInhziNhyk9xiI2oHj01K+4Pftiul6X5iqvnfVTHFaFkodUCkPXPJiVBbsref/7339W6yWTSe6++26+9KUv8eMf/xjXDe3Ia9as4Y477pgyHqC9vZ2vfOUrZ7W/zZs386EPfYh77rmHPXv2VMTe7du3c+edd7J27dop17vyyiu5++67ueeee3jmmWeA8GL16quv5s1vfjONjbpHWaPRaDQrF1XMg+Mw5rTTMKHjekNbcs7bsdZuoC3j05fMsb+1fHEs4MCjX6TrJTfNaRtyLMPpZDtX9DsIFAEKcyzPlp6H5/6GNJrlTGfnou/ymWSJS3t9rjz5MI+v3YnEKA9HDucnSzZP/9prSZCF2kM+m4UHHoAfdXPMEVz8pX9c9LZPROZDd9Rgsm3SPN+ex3iACZxoMLjQG+RQC3T2hs49iLKjv5pDKByHlikE1s/+zpf5h4/9FpcNePxqbXhLdt2J8joizWNTVBQp2GGxQFNByjdYu0rfbywUHa0buHDkCMeaNlC0QyF70/BhnmpbhWdVpZyvX7aG4cTFBCJ07OVGnXavrwAAw+1JREFUIoiWVVNuc33RBiRFq4GTTZ348RjPPPIYlzxv6li5ol8vpAoV0FgcJR1tIjahiNRMxZ7Go3wK/3ITR0YPc7D1GL+4IHSoSt8nV+phrxpmxymTb1+vuFBsqhTQenT/T1m/cwdKKZQhKNiraBi3JtZwsum6yvOSIVGjg1y652s4QdO07ZoKYRj8/+y9d3gc13m+fZ9p2xeLDvbexAJQvUuQZcu9xyVNduI4sdMcJ46c2Ekc/764pCh27LgmjpXEVlziuBfZFiRLsroEkpIo9o5eFtt3p5zvj1nsYgmABEmAAKlz69pL2NkzM2eWOzNnnvO+z5tJDnMkUEdvUKeYz9EqTNxQGGOCpYZ0HdIzLZikmDM67+pkzbDLur4AoVXvnLLN5h7/x5Tpe4j6lWvIllwmX6lnhizHkY+GNyHLYl/JCvJUwwpuOsttXpQ4DrZuVIrT0tGBZRoITWeflWW5PUo64N9kNAkD5UJXwmiGKQRWz3HJ2y6R00zkKOZQYD0XgsEgb3/723nLW97C8ePHCYVCLF26dM7219bWxvvf/36SySQDAwM0NjbS2Nh42vVWr17Nhz/8YYaGhhgZGaG1tVUJqwqFQqG4KJCFPLS1sX13mN6Tgmy2blwx4+0IwyBwYohoQ61BvidnXvXay6Q4EGsjZJcjwmyH5tEBNdBTKM6BPZESvYbGklQP2/p7GAnBmuE17G6tij2HG1ppcE46V12XHW0wqLsMB/O8+Dz3eyrkW97MWChM3ggRdvyq4xuGoaNnBy+78xdztt90QKCJPPH8CeAku5Lyw78wLZqmEFj1phZ0z6E5cxio2pE90wJMX4eb8U/rs2OzVrhLMZmiIdClx+ufuY9UoB5bugxEMpje1dgTYuVGIrXp70JCXaZ1ym1u6CnhYfBs2xWsH6mjOQddn/8aSz+1eUqBtHRSBqh0CyweO8GOtoQf7TzuhSE9GsJT2xJMRJeCxpykrvAM39gcRffqaDu2i8/fewRPgG67/O/d8OFfz3O08igsyWTzyFIRLIuc1QwnJaCsHfGtLB5a7k8yHAwW+c0jOwk54qwmj6Tn8f36Eb7/MiiEDOy/yiAK+ZoiV0hJJlvA9eSCsCl5IaN7kpIWYkajOsclW5pB5HF395SLpYBUqA5Hr+7N0A32x+q5UcpTFlR/ISHd2ghWITSsciZHSUhC9ijgC6yZUoaB0RxCgqVNrYFJ1yFXUgLrTFjQruihUIh169bNqbg6kUQiwfr162ckrk6kqamJ9evXK3FVoVAoFBcNXj7P7R23kzcj5Awqr7ah3YTjp05rnITrERvqq0Svgi+wzrQKuZtK0Rf1I4LyBqRMDyt/4Mz6oFAoamhf1MHrf5lixYEkdSdGyff20jp0qKbNQEOEIw2LKu93tMFTbZK0KQlqJkKEyC4AbzZP1zhR18aBBl9v2t8AhxMFPv0mfU4fuLvedh/JkE4if7hGEM2Z+JFDuk48EpxSCDWXrSSp22zufxbL2UukNEhbegCAVUlo7/dFq4m9Hw8cdAWs6z08Z8elgI+/8pP4JWCgKTdK0EmT01zq81nCjiDsCHBdPGHiClg/DCHbf5nFOvKlyfZ4ptQYDSUoGtVnRlvTODQ4dcG4klNrfRcPx7is51jl/d5G2NsE9y1NE7JOL7abUkPYDmG7wOXH7+NVT3ydNz76Sy4dMtAk7G0S7G0S9NfVntPZXBEv50+S5q2Wyr5d4b/Go12vOwrXHxPUN7XxqT9ewvs+ctNZeUpH3cnHIgJBFodjuJ6L67k8dOwhPLs0M7FOMafoEvJaddIh5PgFBsOlApHiID3RUQ40+FYo40Ld2SIPH6InNjm8vzQ6zHBWRTRXONkiQNOwygURbTxevm+UkOPfZ7b0uvR89t+5Md9K3ohNntwr119Q59rMWNACq0KhUCgUinnC8R+wcqZf4ONAA3Q3OXzxDVOnPp6KvU2CUStTSW/NWCCkIDvDQXY6laZgWJWUW0eDVu/UxUEUiguKcmX681HgaiJRK8qexQECl13N9o03ExkZJpGvTanPGbXZwBP/Xlw02fXUc+elr6dC6ho9cT9qMGvB0r48Vz29mx++874537e7aiXR4gBBu1hZJiSVys3N9VNbqgjd4N7oKPlsknf87JfsyX+Tm7vvZc2ohyarwqoErj76ANcd7a98+U0jR7h0/zNzd1AKtHAUISXDIbB18IQkLVxMN8O6Ycm6YQnSjzReP1wrhBezKfozk33GA1LjaL0f6by/wb+vPhVKMjwyNmUf7JME1pLpEi3liBX6qwslOLmZ2eWIrvsweoYJ9I3yWGAUe3Rkynb//Op/qnnv2Ta5dJasGcTW/QlWfZr5USE0pNWHEDOvizKJ6ORzRgiBbbhosir+StsmpQpdzTu6BzmtduJ93dBeXvH8D9kw/BAhe8LvzHXIztQ7N5PxX+UC575NhcZwODGpqWsZnBibubf/xY50ndoCeZpGwNDAdbn3P6G9t59iQOCVo78dTRIVbRSN6r+jV55AiTuDbDDzRFX06oxQAqtCoVAoFIpJSLuElJCzwuQMWDVgs/lIgf9683+c1fasUo6I7SDK/2kSUoXTR75J12E4WxUuoiVYFWrmuoZ1Z9UPhUIxPdvGQixK97GnEbzyU8JE4chwU4RLwzXrPP7sYX6xr59zprOz+joDxr0hx4JR2vv8h33DkTSN9Z17n2bAibjGITPLytFjlQmkoIMf9eM4rF8+dbo4QPDaGzAxsDD5+aEbaX70aZYme2ratGQO0pQd5OqjDzFS/ArXPPB5Nu36Pua2yQW9FLOHFk/UvP/VXfCBgfVkIiU8rSpMFMzquTJOQAr6U7Vij3Rd3vlskKOJ2sxMCaRSkycMpZSU3FqRsmD6ov3S5NOESoOYTpZS7hE0+9ik9afDEBpmtshxs8CfvjnBa75wM9g2l/ZKLj/hcfkJj1AoiNA1hGEgDMP3O01l6I80IvCjdIvCZUTL42opP6q6/B1or3ktP/+tL9J1e1fF+/VMee/7OyiEDAohg6hVFVv/8Kb3UZ/LEilJggUH6dhkVKGreUeTUNL8lP0tvS66B7FSEluH2w6AOaFompxBJGTnXZ08Hc2Q1idMwnd2Im/pBClJhuLESoD0hX5N+lGZPX/+gTO+f1ysSMehJP0oe1zXj2A1tEpmhem6WG41ct4VkhWpVeQszb+elW/8m/q+x6qxx7nKSk9ZrFExGSVDKxQKhUKhmIR0bEr/dCfP3/wSVo5KXF0gAgFiwbMbOghPYjo5CPipkZqEdMGB07jreJkMI//1NWiqehQ2FbMoly2FYvYRUrJs+DBCbmCqOIx4/gRSwHCkEbd8Ej54sAujpY31rXW0xYPnt8MAjo2tG2StcGWRhySaT56f/QvBF9ak+IuHduBetpb2fv+hXwJLR3tZ2TK9pUrX7V1we/W9lh7j0r4d2LoA4rSljrLpyE5KI0kEgmTB5U3vTAAwescZiFejo1Xh4TxHSV+o6HWJyt/DIUAIYv/f3+H+z7urjYQgasPhelg5Kn1fVMBzXQZGs7Cyajvn5TIMh+sYjEQm7SuVyk5ahutiy9o7Xd70BVfLzbF47AF+9RmQI2NsG4nCv53Z8f3sPwXGjR3wicm/Bz0YwpBFHFH2uXQcMpksQ+F6orbg2VbI65Ieq4cP/ewZPG0JeStEa2aEVdf+qS/knCPjwmpHW0dlmRYMYbpZCmZ54GDbM5qoVcwtuifJiQAAB+r9uaWg7YuqruNgeBMmGxx3RtlLf/xih0/8CMBhvMS5BDwEqaBvR1Bzdgg4EarDRaCcqYGTPVjHLQLK0eHCNMsCa9naIZmkN14t7h6yIejkCTsOGHrFJgBDyYenQ31DCoVCoVAoJiEdh4wVpn3QH6oar30T5qJlZ21wL6WH5WYYV1R1CWMzeDByR4c4HAlU0pI1KWkoTfEwqlAozo2uLujsZKNuEyw9hsSvCr6/AbYOQHg4RUQ/QEBK+mKLyFsJABzPwRnsZ29/6twE1mmKmpwOr5AnHYggJz5uS0mkMLWv5WzTdXsXnXd10qM/SmvqAQLOlUCARCHFjXseOSMPWC0axxCSK489SmsG7t4K773F4W+/LdE1DUMzagSnM+uoElbPBGFauMUibjhQXiIxFi/HlVk06eEJX0Qcv0emdafiRVoIGgyMZvCkRCv/+3vpFEfrFnGgUSCQ5CfcSjO5Qk1b8O/BJwusmYCLZju89IDB99aD4ULRcaDjyhkfV+zK6097rolAEN0r4JSjEqXjkM3kGQzXV45XjyRo3ruL5SkH0zvqLyu5mIuXzbgvZ4oIRbDcaqEr6dhklEXA/CNNPFGVNVO6DaMDZPJJwliYbp684d9Hup76X3qfK/CZnYemjXDu7uvmtaUIoZYo0nPpHtxFR/NWpJCkAxHc8rl3sktFSdc4Ea5n+Vwd5wWEdBxKmuELowCibBFQRniyEsG6v17SUU0UI1Lyx+iJ3DFKOoxEx7dpI5TAelrUN6RQKBQKhWIydomMWY0IQ9OIBPSzrtYrPYnlVNMgNQljudMXJHCHBkgF4mTLmUnx215L/YuugrYzLLSlUCgm8d73dwDUPOjqUhAp9XDL/gd4fNnlHGgIkXPy3Lj3Ee5YeoL2Uh1beu9nx5IbKBkNADx44F5iS5dy/drmGpHoZDrvqqZvnvxw3fmG6vXhTKRAmc+TCkyMCpQEi1kM7/wJL123d+G9t4GvOQP8+tM/RBAgZBeIjiTPaDtCCOxcDtMwEEBBeDwfyvO636ujo62Dx88y5VpxdhQzafSgidA0nFIRY8lybM3jcDxLyYiyYQii01iNlgoFRrIlmqK+QOulxxgOJSoC5YOLq79P1/YL/0QDEx7NXQcbMUEgEUjN5XBxGCsf47ZndJJOgWuOTFBGZsIMhHYRDGJ6OYrUA75Vz2i2wEiomnIiXnIb1378LzFXNaDFIgjHJTKaR+hzJy+IUAjTqU6wSschpSwC5h3pBWreF3ARqSEc6SENA2OCRYAhQQoTKaePcm4o6WwrhRA6aLrGkqzE/w+SEf832N4H3W1++70NviBYFMN8dX2eB2f9CC9A3CmKXJ0ksJpuimhRont+UbJxsha0JtM0e7txNSi5Je7qvos/ePVbIRg6jwdxYaIEVoVCoVAoFJOQjk3WmjCQ0vTah78z3qCH5WbLQ2QYK4wxlj1FQYJyOqvde5DMFS+vdiMUpj5kTreWQqGYIdNFDxnlqLnlyUGWjP2Yl+yL0dq0mODKJfzDj/Zz49uG+cFXoPtVD9GeeVV5LYfsaJL+VIFFdef3AUwW8qSsSCV10ZMlYsXzE706Ec2T3B9N8vZAHKF5rB8uYIydebT9H//pZv6/Dz/EmGFw60NF3v+1s6+4rTg3Ll19HQ8cvA+haXieh9B1FjUsJ93nC6whB5D+v8/BtQmyBb9YlQ7IUpGBTLEisLqpsRqBciIPHLqP1xWurbnHStfB7rofKItXUvKx2z4G/+9LFFP+fpyztOw5HVogiOlO8IV1HPaPiUrULoBmWbREAzwbdcDxixhtz59DYasJTHdt0k6KYMUu+VZDinlFysDENwgvjyb934LnOjUerJbU0HBBBk7eTIXVORMBfG+9bwNgeBp/MORLrKPBeOVa31GIkiZZWS8gNTyrmXTBJhZ8YY8THdvG0arXh4pFQBnN9bDcNLtaqNj9REv43rbFEluefZzHtntMvPtIR9lxzARV5EqhUCgUCsUkpG2TM6vpvkLTzlpgvXz9TVy69npiVB+EdE0nlSsi5clJXhP6gCRphWpSz7RIlERIGe0rFHOFITWOBYqUdHA1SctAP8Hyebo9X8fluQR/87oE0dUrSBTHOFDv++49su9eDg7nTrP12ccr5MhoVrWYh5SEz5M9QA2joyQ6ruY9f7SJ9/z+BoJ9o2dkDzBO1+1dBAoORqZQjV5UzA9dXb7vo1cVDm0dHDFC3iinKEsJUtLe2o4hBLrm368e3vUD/uorf8Yrv3AzAMmxNAWjKioJw6gUy9I9OVkodBzs4dHqeyGwDB1d0xEShIRQYW7EdxEI1hTAka5D6aTCRIloEMvQ2H7MZnuPx/ae2RFXT9mvkO/Bun4Y1g+DfORh8raD4879vhXT8MCDyCP9MDZWXebleMWvwSt+DW773TAaLpr0xTkBWJ5WK8qexIr8ZHFUIpECxsr+q+BnQq0ePkhJr0ZxtznBebkPLSQ67+rkFf/5SiQTbBROjmB1PQyZR/eqoul4219e3oZpDfJboyt4zZEAy+uWc3vH7UpgnSFKYFUoFAqFQjEZxyFnTohE0/Wz9l+lqwvu7eL2zt/B0AyM8qx6qeRQsGsfjGR9Pbe+wyL16C9447KH2dXaXPN5OB4nZKkSBgrFXCEQ/KRuFKtnCKtniOBwbYXzrh0ddO3o4PNv+xbLM4MIyg/NLhztT573/spCnmPxCBlTkjEl9S97PZfd/QMuPzFPoosQyLO0UlEsfP6/2/6BoD1cXSCEL37G64hmn8H1XIIFB30kSfyYwWVPCI4f6aF3dPpoZt2D9Emp7tJxsI2TitQYOtvXXo9Wrpw+V4hAENObcN5LiXzi8eokBlAXPf+pwidHsEpASjnpu1OcZ7TaSW8pq4Kno8ErnnOJF/KV32zQE5NsBSbSUKqO8STgGAJP+CLrRIFV3NRJXWGMSLG3sswwTP7wOx+qsaN5IWJw0veraQQmRrA6HrZbIuiMsX7C5ay9X/Dx1/8+V7RsAQRt0TZu7yhXYnTUeTYTlEWAQqFQKBSKSUi7RHZCBCuaRuQchE0hBFHTAOnBeJqh55Iq2hXBVLoOuaYo7TTyw1u205BIsNcJEHL8B1DtZS+nrT58ir0oFIrZYH1SxyiMRxxN/bigJRro+NW38r0nn64sS44kyRSdqaPdOzuhvdv/e3vHrPVVFvLkrAkerIZBbI5Sp0/HdKnNZ8qL/6iaSj56inaKuScWTtS8F5ZFwB5Bkw5Zy6DO8e9f7cvq2f1AD8nwZsAXQDOWxjOBzfzpZz5K+6BOtCxOotfeSw0PUvna6DBpl7An+plK/BTfri68JXMbIyXGLQKk9AXkKYjFzv+9WIQjGHgYTpFdLQEMPcPBJ+/ipZe8n/qwymyZDzK6i42OO6Hk1BuftRlcDTHPgA0deM5OLDcPMg4STKlNaxEgXYffeNLm3uWQs5oYCa/CkCWyQyUCrk0qGK02NnQ2BhNEiidIh1cCELKh4WCeE/ndcPtcHvnCpPOuTrr7urlCJmqWa5pWU0NBt12W163hxOgI0IRXvqQYaCxu9tcNub4XOC95DUI30GJTW5woalECq0KhUCgUiklIx66JYBWaTuRcPFgB3TIxZBFHlCsTuy75UjXFsfT8MxyOS/rqrmOfE6Nk1Brvi3CE9c3RkzerUChmmcd3Xw/XnrqN0DQaW1tYN1DwqxUPJfEuSdOXKrC2fJ6ORxFNFB4zpQz7+7pnra9OPk9uQkE+YZjEX+D+e4rZY7wQHPjF14QZwNFd4vmd6N42dKmxduQol61t43+9DLft6+HhZYsAuPo4PLK0nN7sOhWvQx0w3Qxu+V4ogFQ2X7NfX2Cd8DuOxzANfwPjNgRzhTAMXM3F9LLYun8u7wgmWVMOHo16EItFTrGFOepXIMiRRgNbZsmbAcAhlxoioyJY5xdRe701nNoCpkJKLCcNtPqfS/C8qcdysuSvmwwt4VDjVbT3+8vvjjrc7PbjTrCMErpB2ze+hf6HqwEbVzNBQsI1GIq8hFTBfsHeC/STIlgtQ6uxrNFsl75MH9GCgcFqcuXCimvf9CYaI/5khTleiKy57Xx1+6JACawKhUKhUCgm4Z3kwepHsJ7jsMG00L0CjlYWbj2XnO0LrFJKSvueY2/TejYOx4gX/KjV8Vl1XQqWN0ZZ0aAiWBWKeaOjo6YKudW6iOPRQdJBX1Das/dnbL10C2ubo9R/vJ5MyU8z7rzLj14VmQxG1CKYzNC1SmBoBjcsvx7vRz/iynwddZ7JgFHCy+fQQjM719OZHDXZ0oZB7Bwng+abjraO+e6CoszJUclaKIQmBPHSUW7bc5yVhQCGdDFidRSEy4nIHlyxCH3Cj3LrgO+Zmiv/LIVwiZWOUQisr7RJZU4q+mjbNVXAxXgEK7D92Nx7IdYnFuMKjTGjKoSFyjqmcOdHuBJCMBBwaM1mCdkNALSkPdIF5Q05n4gJAquOwHBtvvg92F6Iwr91UfyrRQTtZKWNYbtIL14pZgpU7ivSLmHoAdpyHRz1/4n9iXYBXaFlTHAVJRgwCYcC/KhhhJeOHeNE3WpCjt9irR3nib093LJtBXR20jkhe2K2Mg0WMrqsPT8toyxMj/o5EQLYdccSbkgWuPr4Lzlcv5JoLsXLbr38rLzDFVWUB6tCoVAoFIoapOtScLyaisFoOpHAuUXNCMvC8CY8RLouuXIEq5ca40t3/zkHG1fXRK1W1kVy5ZpWNfBTKM4XXV3V1zToza2ES0PkDfxXNknf2NQFRhKOzm3ZZm5K1fGrg83UN7ZVzufC4w/S7AZIhQRBM8BvfHCDv1JnZ/U1DSenVgeDAYKm8mlWzA0T02RNz8GQ/j1MBEM4miBsj/Da3cfo6PNFDFdM9kvVizmWBjx0zS9a1d7aTq5QrCnW5JUKvsBa9njlhhtrqoDPNUVDMJY+xFhhDOeB+wEq57mGmLdJjLt+49sUQw55E/ImeLogpSJY55VUwGDNKGQe+QXtvZK1wzbb+6tjNXHf/QSdFNES6BKing5ukOIUkdiyVAI7TtGojcCUmkC6Tk3hv0TZFqKvMUhz+jnihSy65/9GcyZ87gefO2Uh1YsZHYtUEDIBwDCqAusEHkvk6TWKbBwY5JVP/oLX3f8QoeD03riKmXFhT+8qFAqFQqGYdaRjU5C1D3JC1wido2ghTAt9QvED6VUFVuf1rybfEsU1/KhZT4O1I37kgkBy+fFnaFm66Jz2r1AozpJpRFY90UC9l+Fw+b0pDEaHRnHcpZPari4EEEiy1iJcfS0Pr9Rp7j+Ai8AZ6KlpuyJn4mUzaN3d/oKOjmm7lrr76xBprbyPR9QDomLu0OIJAH6jW9KaAWQJLAthmvTEBImi5KodT7BkbIBMIEh3SwTTXYKtVSPKFvU8z7E1OlGzGh0qHYd00al4iZZKNpIJIpWmEzDOn8BaMMF00/6+T/pML7nzlnqtN7XwymfH+K92/33E1Ull8qdeSTGneJqJBFzhi5mmdH2f4fJ1W69vJOim0aTHeHxffV4yZoZpKfq/sXE7mZaMxx+E6ivb1jzY2QZbBvzxpz5hAj4R8ceLHW0d3N/bzfVDD3Go6SUVKw4vlebocIYVc3TcC5nAyRYBgckexT/4dJI/uU0SPOy/F9HE3HfsBYCKYFUoFAqFQlGL45CXtY9UActEO8foUWGa6CdFsOZtP/LEPXyATLCFfY1lW4CSjVbK8OK9v+A1z97DZc8+hdBVVJpCsZAQVoDfe/1fIHQdYfhxG256jJHc5JTdpqzHFT1NLMpdTcFs5pFlDfzfFZdxTXseJ5/n8t61lMwrKFjXkDcacF/1cjpfO0b9O5M0XnX/lFWhpeOQNkNES/gvWxCPnP/q5ooXDuO/89aMxHQBrxoh1x0vkNQddClZMXqYGx//JSfc+5Hu97nsxBMsHTzAumfvZ8Ohx3B0F0NO8Kp0bFKFaiRm8f1/6UfrjUfg6RrWeRRYP3Tbx4hQqPi9tvf551isBNH6uvPal4kYi5YSy2dqlqWSqXnpi8K3rpATPVgNA2vDerj++soiLdGAhiRS8oVwCVyxa5DUM8/Bgw/C+EQaEHAkvbGWmn2sKU+2V7KbXBdcl/pY9Vp/pF4n4Ga45lgWXVJ+CX757DGmSIq66LG8WoE1MIXAOo7KC5tdVASrQqFQKBSKGqRjUzwpgjUUOPdoFWEFMLwJEaz33EP205+C7p8im2NkAv6gWvNAczzaevdSHNxNDsmKUZUCqFAsREJLl2N6GQzb90z1CgWGs8WaNroriXg6zzdvQpYnavImgMbLjm/h59/8BT2LtpIsPy9nw8v4ZXOKDe4OLhsKYwtJf2ryY7I3NkraDEIk4S9ob6cufv6L7yheWBwOFQGTkgEhI4D1xS8DsGbZNv4nex+/UTKQrkerFefJpfCxb/eB6KfFdvHKlbz/7Ja/4l0/vJuiUc9Dxx7iprbFNcWaiidNKArDxDyPFgEiFMZ0M5OX1yVY/r73nLd+TNq/phHKjdUsK4ylKDougSnSoBVzi45AilpJyfRqr9VCCPYEcrx4LAtEyFiA0BgNRXCCJnpZ4evu60aOhRiMbgHw201FedKhPubfc7pu76Lzrk4ePPIwvz1yEFdsHW/I6MgoJ8J+ROyd/5uBn3TDlztPaX1zoaNJMLGQ4F9vQlECoeCUbf/5J4KQLTHqErUfXMTfz1yjBFaFQqFQKBQ1SNumIAVEJxS3ONcCV/gVgHU5IYI1meSjV0g+ui7JfzwWYP1oM86YP9I2NZOmsR5czz3n/SoUirlDq6vndTtH+MEGXx392TPfoeOaS2vaxEqSnJnADjWjSSopnFsHIO4F6A0GEFQfqF3PYWf9YkzWUSDNcPQKnBN1/GDnCV60qa3iseoM9JLOOyB9IUgEgtRFL/wI1hdCEZYLmb31fuQeQCmfI7Z5O1AWet5TT7iUATTo6KCjDfLWgwDckE5U0qbzgSCml6OIL/5IxyE1oVhToX8Q4uv8N1JiBaxzziI5E7RQGA2PG/cP4WqJynLxolvZtqRu+hXPA1Z2rFJwqxA08LIZ8iUlsM4HuhTIkyQl63OfhcWNNcu6Vmu87li28l4IweCKZXymY4is4bHj4C/JGB6U4rhl//+J7qk7yoXs2/uqEZetDdUxatftXVz6iMbyIzuwti6npPu/US+TqQisawddGMlAX/e5H/gCZlHBwBMnFbkKX/j3xQsFJbAqFAqFQqGoZdyDtd03OROGcc7+q+BHsE60CJBCgBZCl4LRaDOOVh6W1NUh3vgWGn77P855nwqF4gw4i6gVva6e1mwSWAJAQGoMpWo9ERN5yaVDa/jaJsHKUVmOXoVIyffXcwW091fbCwQyk2GkZTOOrrF50I9KPfCZzyLe9uu87KoNCCGw+3pIGgHccqEhKxKdt+I7ihcOx0IO5vEk0jIw8iW0aKzm8/e+wRd+uj7RRRfA7eUPJhRrE8EQhlsVnHAc0hMsArLBWkEkEjq/3sJaOMpvPFZkb+NxDjckAF/YWtZaTyI0P/6r42xOmeiegzceOem65G2XxLz26oXJzdkGeiIGuqSiiJpTFEoaM1xixSztff4139AMhiL19MQ34wmD61LPcDSQZEN+EY4GhhRES5KQXSBSSrG/odY2oDUzRPAk0fCp52/CTj1LuNiDZfoC65M7fkjrSBbqMn7kq+tCZnJk9sXCnR/r5p+u8dBi1fughl/8cTpyJsRP4XOuODPUCEShUCgUCkUNk4pcafrsCKyBIIZXZEtvOSrV8whInWV2PQWzpaY6bHNDnFA4CEyd1qRQKBYGWjxBQ35Cyq6uMzgyBvjXjJ9+yaW54QBPLruV9j5qw5JOIlq2pMyG/XUDXoyCBYcTftG7TC7JgS//NzuXvJdtS+vJfPxjOA1bKtcOLRIjHlSPN4q5paOtA82+D2ynEsk6TtcnRqdfccIEhnhuB6aXq7wfL3IFIKUkZ9WKR+HzLbDG6mjMSdZygKAT5FhiMXWFLDdfsfG89mMqxIaNGLLIilEDT3ORIw+Tf9XN892tFxyyVOR9z0R5zy1GzXU9YE0W4B0NwoWU77EPhAoOyVAdhxp9ITTEEt587Ge0FJaRDoBE4gloyo6wJLWbR5feyCVDJgIwPIfrTjyDODmiu6sLvVAg9kdXMGpuIm8AuDzfGOHj9+i89i0urpAI4TLjqcQJkyIXStr8+7sjfPYaq/L9CCmwprAX0eP+d38gDtsvkGO7EFAjEIVCoVAoFDVULALKCE2vpOSeCyIQxHXSRIseEoGnCdaUIqx1Wni+eQmMDwavvIqlDVG27z7Fg6pCoVgQCMOgNZ8iY0lA4HkuTi6PQQPgC68lK0IyGKuZREnknsd01zOx5u5Ei4CM5lsIPLjMX7a/obw/khiPPUujXEtyNAMN1b4E4nEiKoJVcR7Y2er/P+wILj+L9UUwhDFBYKVsESClBLtEzgrWeFBGznPxNhGJIlwPQ8L23p1s791JIuVihc+v0Dtl3wDDLQJVv+W8reyEziudnXh4uG2L8LTa8aGpTW1loZX62NHigdCqBavwtdmQE2AstA6hNROc8E/ZNtZDXX6UZSM/44ZD9SwZGybkFAkVpp6p04JBMmKQjUMezzf69xY9FKa0fC0vKtkcC5g8z95zOvSFTKaUIRzQ2TAUwtX970hYTDmGf+/7Oyp/TyevTiwuqaxrZoYagSgUCoVCoahlUgSrNisRrFokikCyu6mIowVZM+IvP9x4PatHA8Q8fx9aIMjiOhW5qlBcKIT+9V8xvvdflIyyn2Qxj0kLcIht/ZIjzYlqYyHYGxzjlSd28bpnDrG7+Xpypp9irXk27f2wr7EaAaWf9BydtuDxXT/hgf1f5Z2h+nKUkh/xtLYhdl59KhUvTLpu7+Lpj9Wf0zZEMHRSBKuN7XoUHQ8znycdqIqHUkAsGj6n/Z1x/4TAGE4hI3WAwBjNYG669LTrnQ+EpLZgJpAv2tOvoJgTpADnJHFVcz2MKaIlR+8YJffT62lwhhkxm6fc3mB0PVEHpAte+TK++tmdNGXy3NsBf/R8EMMKYGULmFdcPW2/tFKJaDEDxP1+Irnr0itwNf+cWuJtJ1O0ic5C8dYFiaaTtSIThGpB3RS2HkownRvOXylChUKhUCgUFwTStil4E0QKXSNonvuQQYtE+c2nPSyn1p9x5ViAkONHrQFEY2EW1ylDfoXiQkGrqyfoDFXee8UiBr5nngwF6Y83VRvrOnWL4nx87SCRgwcRyR/y6ud2s2boMIvGHqQvcgJXlEUTA67vMcjrHkF7jLEguBqMFZJEcnUMxBqJlnxrgURR0BKd/+g6xQuD976/o/I6G7RgiF9/YoxtPTZbel2k6yAlpAoO7kAPKatawAcpSUTP/6SjkS2Q6+/neKqfg3oG0XXfee/DVIgf/ohAfRyBQPeATIZcvnDa9RSzi4ckr1s1yzTbJWhMPV7U7vwEmnz+VC4x7G+AvAk5wxfRw8XqeFHPFfCSY2j50il/i//6t09Snx/zQ50FhG2w9eqEhaXV8cjOgzM5xAuSghXFE9V/AyGhLniRiskLECWwKhQKhUKhqOFkD1YxWx6spoXnephuNWpn7Sho0hdScgZkdJdrVzagT5NiplAoFh5aXT1WaRA5/t8D97NkNMYVxwUyHqEvWo1Y0lyPf3n133H96ps43GTwcHSIlTvvZdFzP+SZ4DGixQHWD0OsLJziOPzRG97JichDNfv0hOFvVwgQAu3Xf5OWmBJYFRcGIhDknlUuh+M59tfLcgEeh3TRpvSu3yWtV3/LnutOGYF2PnjPSyXvuc3jfR+5aV72PxXCCvCKNZ1EXZ2o649N8vnSPPfqhYcUknz/UI3/ajiXnzKCFXyPbK80xNLkY3iav5oj/CKHa0Z8PXQiRW+o5v3zTfDUYsGu9XWn7Jfe1Ex9uYDc+mF8Ef4k9h/px3an+OAiIOLGar7LgOfMSpCEYmYoiwCFQqFQKBQ1uLZNaYIHK5pG0Dh3gRVAyxexXL+C695GX2AN21TSfDcOPM/qlYtmZV8KheL8oNfVE3SG0T3fhzWdH2N9KMaKwhYy0SSpYLXKul5yWFwX9NMT31PPv/8gg8QBKfnotzJ8++YjNBZXUzL8qNclqX4u3bScby67l9/61IfJaX6q9MojYzWeriIUoVlFsCrOE+eaXiuCQaSAK07kyAYiuP2/QD69i/Rfv5cvLD5OnPZqY02flwi02DU38YtD5TdfWjjpxELXCekaT7VUzTqXF4qnWEMxF7x/1UFecWBp5b1n24RL00cSa3W+rUZd4TgbBw32Na0nUYqRCeno0qG9D55aREWw7ZXHAL/KPQJe8kcJwLcbOBUiFKE13Q9snFSEbhw7NUZvqsDy+vNrvTHXRK0oqVC8ZlldKT+5INgMUTYCZ44SWBUKhUKhUNRQONnLbJY8WAGcXI5IaYTBKT5blOph/dGn0OOJWdmXQqE4P2jxBL/7RIlPXjtG3kwgy0/IRxqu5f+2ToggkpJgfT2NEWvabT2XcHn94w8ixGIMz2XV0YME6hvwDIO3bejkCweeBCBecKorGQbRWISINTvXKYVirhG6gS08nmvK0pxvBiQBIRl7w5sIb/AjvsPln3hduG5+ItAWcGXxUMAgH6ye7/mCimCdUzqrxY7GfxdhT2M0VPUiltIjUsydvGYFvb5akXBX82GC3lESdjMrRq8jZPu66rgeqqWTFK3n8MrZTPd9uw5GZ1b4VAjBPTfGcXM9uNrims/GC8d1H32U65JXXXQC69ZF27jHrPOzPwCEoKE0/b+JYvZRAqtCoVAoFIoaTi4Woek6gVl6uPMcG8PpZXtvCkePVyJXl48eZvOBRyimx2ZlPwqF4vwhDAPPsfHoJ28mKstPTs/UhMay9/1xtRBVRwfbgSs2Pcj+FVE62joA0H/4CMvyY8SyLsblV/r7iMZYPNGGckJkkhaN0hIPnXWUjkIxH/SaJcLlVGYBuOkUo/E6jtUZrEqVG3mSRfmU+m2fRNCqjehVRa7OH/Ufr+ene8dgcTNH65dVP5CS1vTwtOsJ3eBwsMAdD/qZBkEJH+8cY8Pgbk7Ubaq0i2RG2Lbrx/zopgKHVvp2ANvL94aZkgwKGgeeJlyqQxJBl9VbxviZNDKchFUtZ7TdhY4jPEaDdRUhWUqXpo///fx26gWGElgVCoVCoVDUcPKDimXqs1aZOx/U6TEKvHPffeSsxeRMh7axHnIDfbhS+n6KCoXigsPTDEKlQ2wdXE16qurMQqC/5dfYtniCf145Eip6Vycd44vK1gEAGAaUi5kIIVjUGENIDylqJ3yEFCxJqMJ4iguLvYE87zmY5fkWfyJCCo++eAM5K8beRn+CQjoui3PZ+e7qgiMUrI2CL5RsXE8q//Y5onPNgxPe+QXYdBGnqFVtWaTnsXLo+Cm384u6NHgWaALNdjFbFrPxyd1s6zlBX7yVwWCeG5/ZjZvP8MlfJs5YWB3n//3K5/iLf7yNX9l5L7sWbeJgfYJrjqf58dpV6GWJdfRTn4Wj3f4K00Vrd3dDxre1orNzQUd1A5QEJCdYBEjXO2XGiGL2UQKrQqFQKBSKGgq2W/M+ZM7ucOGglQOCrBo5ymgI3HyOd71CEg0nYHsHC3v4qlAopmJL/QZ0bw8v3vsQjy3vYG9jopLiDKBLl87NS6csRDVTn7dAXYKQM0jObMXTQC97RYuXv4Ildee/yrpCcS44oSDxfLJmWcmycHQTB/AENEVaWDz89Lz0byETPklgxXUpOC4RS8kbc81YYYycIUGP1GQSmHaBpmzylOuOmA57iv3ousG1IxEy5dtBfSFFQyHFk4sEr3uTS0/A4PrlHXR9+ez6aLT4Xv7Rks01R3ZyIAFL8kvJmasAiVcYI2nF8V3DT83Jka8LmdFACFdojPdaei4NYSWwnk9UOTGFQqFQKF7IdHZWX2XyJRt27Ki8QrP8wNJnlli6b5jwSI5lR9Ncsj9dEVcVCsWFiei6j580Z2jNDPPK3ffSmL6HweAulo4dY93QPl6z8ydsWLNkZhsbHa2+JqDF6mjI76bjRBHNo1Lkqqk+Pm9V1hWKs+XenR3cs75ArDiIp4GrgURy/VG4/ijUBROE3vY2Gn/03fnu6oIjGLRqitxJ1yFfck+xhmI2uPMHDtGiRNd0ikas5rNQIQ0dHaffiJS4jo0APvK6zyAkjIZhJAyv2gtr1lzJ9cuvP6cCS3p9I16phKODo8M7HrbZpEdr2tiaRsY4dWFETxNk2+pIr2gif2Qv0vNO2X4+kVIyYkVqlgXyaYKzVENBMTPUFI9CofCpr5qUz9REXKFQXJzkv/ZNsPwK3kSjBEOzFxn21399PYf6uhGFJMFUCQGIm26m6xMqblWhuNBp2Xg50UP3I3WNPaUBri+muKRfQwCR0ew5+0hq8ToCboprDj3AvubLyQSi1IdMbtm2XHlUKi5I/vLoCvYXj3Ln5X6hq7UjELFBINCkYP2iulmz6LmY0EMhdFnEFeXxieOSt5XAOlcICXf+VLB6VKBJidA1MoFW6iqOUpJoLgn6mclL5rJVmBP+2UqFHPe+/b7qgnNIyf9lOInVqyM8j1BewgabmO3gCpN8UEcCo1aEmFOcdhvFeIgvXe4L+bcdgJUvvpmApy9IqwDn2GGyZu14PZJLzk9nXsAogVWhUADQ+YZM5e+Fd8tQKBRzhezurkl7kp5HIZWGRMJfkMkQCs9N6m3WAk1CfAEOVBUKxZkz0T81a2W4Rx/gZckg0nFIbLz2nLevxXz/1ob8CK967h5s3SK6aCn1iehp1lQoFiBdXVj5HEtf/WIsJ0vJ8KPPPE2gC536V76Uy5fVn2YjL0yEFUT3SriaPz6RrqME1jlC2iWuKTUQaDYoBF0Wl3IIQ1Iwq37a0pM0J3ugcfkpt6VrOlD1jtGiMQrJJGY4jHRdSunU9Cufab8F6HlfPBUY6IBNioLRgOe5yLEko0dOsPzEnmm34a5diScOAeAh+UzzMb4bPQ53dZ5ThO1cUNr3LHm9agcgpUegmDnFGoq5QAmsCoVCoVDMNfX11UmMG84t7Wm2kFJSePGN2M1h9IJNKOugATKboWDWpkyFI+E564eKOlMoLk66/jfqp4uuGF9w7tc9LZ4g6eVpyQgsFwJuiZCnriGKCxctFGZf33MsbityuPFqQg7ongQcLl/dgmUoR7+pEJaFLidEHroueXvhpm9fyNiHD9Bc9CM+711/A1v0Vn5xRYl0wMLVYP2IP6asy43AzgOn3FbUihJ2xmqWZYRNaGTYfzOLY8L9K6LsafL3dfn66xH//kW0T/4JntYIgNQ0kuH4tOtLKXGFpD8CRb2OH228ip2tUQT7ebr38Vnr52zhjg5TSOcg6ls3eLZNoFSY51698FACq0KhUCgU54GM7kdW7O/rnt+OlLFfdBPFnkMMRBtINcZYmhqm8cRRnBNHKBiByiBXRKMEZzGCtSIu3zFrm1QoFAuJmXjwnSVC19kZywP+pI/wJJan/OUUFzbFfI66Qg/C3Q1sxNEEy0ZOsHbDyvnu2sLFrBVYpedOKtCpmB3ckUEADtev4kBDKwCma7F1ACIl0D0IFBxuHDn9ZMDoHaPwsdqo7L/+6+v527++D6CmMOK5MnrHaM1YUx8ZwnTTyHIBKAeP0VMJrMUiHD2KXCI4UX81CSdCzoQm1nLNcA7peQhtYUyAeMUCsljwx+/jy2yHgJOfx169MFECq0KhAKrij0KhuPjxCnk+sfQYm4x2Djas8i0CDIPghz7N+lQP/YFEpXiEkIJoQBWPUSgUC4PnmgTG8BiuaaCl82j775vvLikU54SUHm/7bi+/8s4Mv3XoMEXNIH7iCLqh7r3TIUwL3Ts5glU9y8wFXsZP2//B+sW05aZuk0gn0eUMI4hPmoTrur2LTqqFVucqx0uEo5huNWVeIhkLx6ZtLwv+wUacNjYPRqgrgFsOsI2wifSRw8RXrZ6j3p4hpZL/v4keuNLDdErz1KEXLkpgVSgUCoXiBYa9/3mKsomdbavY2wgCWD/i0h3JMVRMEClR8WXVXnIbTVHrVJtTKBSK80b7og6M1H2A7+GsUFzoRK0oB1syjJYyeAN5TODyVdfPd7cWNMK00OUE8chTAutc4aXGeNtOjfe9pJ7INHrdkqHemW9wCruY82GdJQIBdM8XWNv74JkWOLxY8B9LStw3RXtZ8KM/R8PLSRT9cbEAJOBpJnsO9nDFAhFYpeNXG7P12kmZgKfOifONElgVCoVCoZhjpIBthRgNromWOYt01s7qzP5s+Bg6vccZCS+jqZw5JPG9pjIWuBOznYSgvj5OLKCGCwqFYoZMvEZNvHbN1uZv7yL9bhXZp7h4eO/7OwCIAjd8fv492i8EhGmie1W1T3qesgiYA6Tj4OUy9EWbWJU0a4qi6l51Mr5j/w6wFvZYUQjB/3vxB3nHYw8z3nNDCopEpmzvFfI4QicVXESiHCyty7LAKgR9owungNS4wPpwG6xIUrFBMFx7Hnv1wmRhnwUKhWIyZyq0zLS9rjzMFIq5ohgLsqnoK5e3DAdwBvswmtvmpS/StkkPDJAOLqoIrOuH4UBDtY2r+YNOXRPcun2VKkSlUCgWFPuXRyt/b5/HfigUs8FCKHx5oSFMC9vJ4Hhl004pKZaUmDTbeNkMUsLOpVsqy6ITspwA3vTkd4iVCnDlwo+6NkIhdDcD+NYABoKC3jBlW1nIMxipZ9uATsb0BctoyRdYs2Gd/kwJKeXCGCM7/nngaWV5TwIIjKDKQDvfKIFVobjA6GzvJlPyZ8xOW7+ws5P0Yw8CEDvdTU9Cs2vioPLtFIrZpqRLhkP+uWU7JYrdj2O8+FXz0hdnsI/9//F1tjWtA2DDMIQcuOXgKPeurmd/vS+4mp7NK/c9RXP99P5UCoVCcUpmIeJ+KsYj/mDu/PoUCsUCxjQJaQJDq8oZxaISWGebzZ9Yy8sHl3HikpvYdsIhZ1R9SDXpce3eR2k6eBy0C0NWEsEwASfJuMBqSQ0vsGzKtjKfo79+UTkIaXL1rXzJJV10iAfnP6NCOg6eBFv4AVPPNEuOLBZ0vWojt8xz315oXBhngkKhqJDJj+EKX6jpvKvzlLPeV667n88dNpGeh9bXfcooj1WRVtZm/Vmu4jNPE9iiYkIUitlAeh7S0PnVZyRF3WIo0sRQb/80CUlzwElR7LkTx3muaU1Nk9VDh7nm2FP0h0wu70mQKAoac6M0jxVRKBQKhUKhWEgI00KTNlt6y7YAIzsobdyycCIKLxJCnsaN+xM82+ogxtPjZYE33Pt/9GtJNg57iAsoNkcLhgiV+gBfVG30LCxtJUXbJWDWZnPKQp7eWPOEBRIQZCzwPJcHD9/PbclbiLfVnb8DmAbp2NgI8iEDKRw2D0BnyuJXP/yd+e7aCw4lsCoUFxgNRcElpTpMBPtz01drlJ7HzbkGfn6VL5p2B1N8ZZq2bnqMtVmLtz7lexkV/uT3sX54P8KcZkZulv0gFYqLGS+bBk1Q1E0+f+VNlIwYheef4YM9SbYuTszZfqWUlHbv4EOLnyGoLyIdbCDw9hsZiaxivRZDL18+vFKJyJHHOF5I8o06hxd3J9EMg5FCgSVHF46/lEKhUCgUCgWA0DQ8YaN5ZXUvk8F1HFxPYuhKYJ0tAp4gbVUzmaTnsls8Szw5wlD9PHbsLBGhMCG7H016gIYEojLA0f2HWbdpDZ13VZ9xv730zxkMxMF1QQO9aCOkAVSLFfSPpFm/AARWHIeinPi798+LgKFN3V4xZyiBVaG4gJCuy/WFRnIhA0cEeNu3Suz7bAfLkv2s/r0ied2/mI7eMYp99CAisIXnmjaiezYrMs/i5bJo4clxc97YaO0CAe5QP8aipVN35MEHZ/vQFIpzZ4EK/17SP7+O1q9lZTIGAjzN5dFnDrO+ZSsBY278j+1D+8i+9Y0Ur76G/tDyms+0CXMzl/3Jn3LrVV8AIHRXJ6u+UrYVsaKgokAUCoVCoVAsQBxxUkl716Xoehi6EpVmi6AryAR8z+toCVK6BHeMna2+vVQleDUanXYbCwkRChPWNZqzIxysb6osHx0YhE212V1juQKy7DYbLflj56bMHlKBTZU2A6PZ89Px0yAdm4LUiFpRBGOAwHBdTHUunHeUwKpQXEA4J44Q8wz6Ao0MxK9lUcHkO5v9z24ctekXJzginsLrvJmj+TSDV1+DJ8DTDXrqLmX42HGaN2yYtF0vl6XkljAcDxDQ14eXz52yL753tkDJL4oFRXe3///OzgUjsnqZFB6CQ/WrapYXBwcZypRYkgiddhud7d2Vv2dyVNJxyO98kv93TQcjoTZ0CauScCgBWweqxQk0Kbl684rqtm/vgttnsAOFQqFQKBSKecShVmCVrkvJ8Yiouj6zRtzR0WSI8Li9rSfBTde0yZkQ7+hYMOPuU6FF4wBEixnAF1g9TZBKTRZK07kJNllCEG5qwBJP1bQZyeQXhC2FdGwKXrUPUkpCdmEee/TCRUnaCsUFhH3kALYWYDh2LQI/fV+WX54waWYlV3iv5tjQCR5qXc6akfKKAvIhiz3Hh6bcrsxmsHSLxhw05oBSCVkqTd3Wccg1xUitaiG1spncQ/ci5QVkvqNQnGdkIcdAtJGiEcDTIGRDpCjxHrifoczceJyWDjzPnT/8AmPBNjIWuBpEyqf0xLP1ysPPUB8NzkkfFAqFQqFQKOaKRYklmJ5LJdzDcyk609unKc6c9T1FSnqgkvn06EqDSMBEGAZCwt5G2NskLghxFaoCa9iuDSSqEVMBpCSVry2aFnfy/OHxGGFHoGt+9plTskkVJhfAOu84NnnpS3sHVtXRvbWJ0D/+/Tx36oWJimBVKGaRib4tpyo+dTZIKXE+/DcML1+NJ0xCjh+FlinP0oYq13aDh1dcxvFEw6RtHBjOc90Us2xe1p+J/OpWAIkm4Z321MJPYcfj2LEgX7g0xKrUauyP/z23FT5Kwz0/nZXjVCjOFg9JMSiQuoa1+5kFc4P79a++kXfUXVJ5/0yL/39XS7IxPYPZ5c5O3FVJAPLmzGbIS7//u6yIraExAA9NcAcw3TwhO4OWz7C0dx9XPrl3poehUCgUC4bZHmMpFIoLjz+76S/4nz3fI1suTiRdl5KrBNbZJEIET1Rj8l5+w2/zGy/6JK9qfzF/+9f3ARBeAPriTNHidQxZLuFSDlcDISEf1MmU/GJp3X3dAARcQeqZn4MsWx8IQeJ9f0ZoYBeln34fKP/mHJvRXIm60DR1S84T0nHISo321nYA9MZmwubcWJApTs1Cef5UKC4KMr+8v/pmltNsZT6LJyAZXk7YhnXDvheMK+C6YxNFGxiINuEJ2N8AeQM84d8G0nmb1G0vp248ZaA82+hlM9xxoI3/SBz23wsJtj25E8A/fOfP+H0ERxquwrbqcXVBTg/zqyWXkKUu5Ir5I3/iAE4siAdkdEHjyBB6Q9Np15trwq5gMDq5H0LC4GgaaDv1Brq70VacuslEvEyagXSesZY4mudHrC7vybP22Z9z4/ARpIBC0CBqXRh+WQqFQqFQKBQnI0wL07XBLFsteb5FgGJ2kHYJg9raHVowSNjSK0LkhYYQgp83ZfmNcgRrzgTPc/n5oYf5dfnKSrsffC7N4a05iJXHylISj4XQ0iEsN0Ve88f10nFI5qd+Zj6fSMch4014DhcasaCS+uYD9a0rFLPMnkY/Abfzrs5ZjbDwMmn60zaXDEaQQNYCkESLR4kVWxCEyFiwZcAXVA4nwPDK4qrun+rSLtETSlBn99VuO5uhJHRO1HWQtZow3Ryj2QKLpuhH0IbeeCOtuXqCDuBIcnGTZ3rHuGLF5KhZheJ8IB2Hf98u8Sr575Ijn38d//wXD8xntwAIuRq9sTh7GqFggqOBwE8vSqVzFGyX4KlmmTMZwp7OR+/TsB0b9p7kL9vZiYuHo3mIu/4bLz3GocQScuU7fMCFrjVFHmgb475PyNpI+zk6ZoVCoVAoFIo5xbSwomEI+iKYdD1lETCLuGNJHD0IEyJUw5EQ2kmZkOPp8hcKI5bLlp1H0C/z30sk2UKWbKl6oB++VedVY+HKeyEldZEgWigETgpXr/c/cGyy5ejXecWxyXjVSGOhaUQDSuqbD9S3rlDMMpr0Rc3ZxkunOFC/zC8sJSXS82g91sOw9WPe/GicA4lLSTduIFLyrQPa+/ArgN94Y2Ub0rE5OpTmdy55gJLwiNzVyb2//jNkIccTTesYjvg3koIZ554em187qRKn9DwCB49wvJzuPB41K4wMi471KYFVMW94L34RssljYtW1eGEab+DOqsB4PjyjAl4YWzdYPwIx16hJ2ZelIkPZEksnFLp69e9E+OKDdQxEm5CeScPSpbykUCTQBFI6yEpNU5+i5rJn7DBHG7bwnb//OxzNYFvjCmJlz9Wrr3g9f3RNB1es+G//kFVqrUKhUCgUigscYVpY3gT1z1MWAbOJl0riGCG8coCm4XhEAr433egdo/Cx+nns3bkRLmS5tNflqUU62bAviWWyvj3ed7+QoeuKBjJWGG38UUJKYkEDEQihe1UbPek4FOz5F1g92yE5MYJV10jMs23BCxUlsCoUs4T0PK7JJbjFDjJglhguzO4N3kmnOJJY5IenCkFdQbKp5wDff9glbY3yq08/x39e5mG667B1AwFYsThLzBJH7bJRq5TsXbqU16WWkAzU8/1DfXi5DJ4nOXzDS+H5X1T2ly447B/KsrE1Vj3GQg4BHK+bHNs6NDiK60l0bX6rKCpemHjCPzX6oxs43LAOw8sRGXoWaZcQZm052RetfpBFTpCC5vKNOa78KV2HhvSE6qvSZdy3CUAWC/SlChWB9Xf/8QY2eE38aMMN/GJlAldARz/saoGwO8jK0QOUhEtg/LhzGVLHD/PQtusZCTUwFoStA1SKESAEWjzB2mZlB6BQKBQKheLiYZLA6nrKImAW8dIpbK1aCFW4HuGLxA5Ocz0ipRxQfc4dy+RZe3CMUF5S1EMId0JEKIJ4wESEwggvjyyXjJWOTW4BCKy5ko0tJzzPCI36sDX9Coo5QwmsCsUsUdr7HGtLYY4EPBpcC23nXqTrcst/31ppcy6RY8MjY9hywoX+zW9hzQPfgbo6Yh0dbPrvu/jN115NU3YvQ5EGslaYdV/4Iu6OR7l712N+NCtwtW7wbMurAFhbMnik+wCLPB1bCrwJ4qh0bI6O5moF1nyeghHD1WPoHqwdKS8XDk42S6pgq4u5Yl6QO55m7BUb6a3bjKuBq1mUgjex51APG9evrLTzCnnWafUkyrYZpT3PENi4de76lc9TMOLUlab+3CsWOTqS4/Ll9UjHYcOgy1FjM6PhxKS2Q5Fm+uMtOKUiLysLw7ldT3PvumsZDTVUigyMR7H7HZCsWdGmzkuFQqFQKBQXFcI0awRW6TnKImAWkfksjghU3gtJbb2Njo7z36lZoKOtg7xxP0Enz0SBNZuvRqYGtDi45bgmIPDWXyVoarjBECEhSWtlGc1xyC8Ai4B8qbbSmKbrF40YfqGhBFaFYpawjx7g9qclH7vOf19v67gDvfB0d7XRORS+6v3XL4DZUnmfiIWJ9vdU3huui2vbjIXA9AZJZCWN6zeQ2b8LvdRP0WyCCRnTnoBw3uFL3/oPXnrJNUCth460bUaytaqQl88yFlxMxK7ZVLl9ibG8ElgV80O+OU4ytGzS8scOj7J+3YqKX1Rx15MkXIP6vAAkH/iH29hx3XocXcxJ6ryXz1I0YuTLd9uop2O4ORzdt+OQxQL9/+/veG/ke8SdEtJoY7TxUlakYVUSDiUmb/NArIX79w9yQ4Pgxx/9OP3RlQAVz1WoDggbQyY3rm+d9eNSKBQKhUKhmE+EaRFwJxQYcj1lETCLeLksUqtaWAlPErEmDDbPg83WXNB1exe8p564WytKjgusejBIMryKurHqZ/XLliCEQASC/Ep3mn+/zF/36RNPcOPWjlntX02thBk+m+RtFx56qPI+9JtrJ3nlKs4PSmBVKGYJ7x/+HgBHC5GzWhEyiTuWJJMfO82ap+eNn7mJlxpLK6IJQFtLrd+p0HXGxkYoWkGEpnHFk70I0+Qfn/pXwiWdotlEe7+fOrx2BPaXV5dIep2qR4vrlWfhpEcqW6hJ+5f5HKlgK31x//3qkQkyq20ztgCqKCpeeEjX/81uHYyTsaC7DTxNkDfgqz/7LJ/63vfAHeKxTxWwD+5lNLSB3sTm8soQ39lDj33fOU2ATNu3fJ6iHqtZFs7vZyy0DqTJg3vuYXNM43jLa9C9IpYXmDR7kTfAFdXFEnhu1z6+ct+HcFduYOsAhBwqIi6AFB6LU0O8+GMfP3UBLYVCoVAoFIoLEdPEeulLoFCetPZcigsgXftiQeZzXDkQZTDgP/eJltaLJyqyo4NwLFKTvZkv+IFFi90IydAylqX95WbRpakuAoAIhQm5xcrzuACKhSKO52FoGvNF/qTffchSMt98ob55hWIWkK7Dl9t6uSzfyFD8WrKmgafBH37rb+koxGhyLEZ0G2nbCPP0htMTZ65+9tr/o/14iLRej16elBUSli5pnrTezYcmDyoKJoRKvSxPbyVR8JeFnKpQW6PlRKNkCr4g/NCxh+hcv5m87VaqEDqpMTZtfS3OuMfLhJky6TqMZgunPTaFYraRxTwlzSBjRSZ9ZkoB+hIo9eOODNL3lW/grN5c88PXrcUsN17D8v+vlaMf7J/VvrmpJPYEgVV4HqY7xlt39HL3tuVs6ZdEy4HirhagvccvHpc3QEp/SiVRKLJhOECkBCChMIbzjf+h1LYBXVaF1fqCpJT6Pr/5WB4pBPGhLIGVX5/V41EoFAqFQqFYCAjTwhQTBnSuS1FFsM4aXiFPtrEF9LJNwGtff/EIrF1dhL/2TfI7HqwsyhZsLE8w0LK4pqlesllc53vRikCQ5dGWivVeppRBlooUbI9oYP4E1lxJQrRabyEUUAWu5ov5+xUoFBcRMpdDAjsWt7N2xCBk+z6Ia/a1sbbUTES0UW9u4+AvHsDNZnCHB5H2NNGenZ2+rcDT3WieJPvGVzA4Gq8WrQGi0QhrZli0Jm8ITC/DkrHUmR+YXSI7wdNlZGi0Kq4CXHedfzGPRmHHDoaT2TPfh0Jxjni5HMlQvPLej/aUBAsOhhRgrfbbvflN7C4Fae/DFyZNKjMNUouwObdp1vuW7u3F06qDHM3xKMhRlo759h6RKbxZO/r8SZCHLtMYcb/Or+z8Ab/z6HcQwM422NEqkVIyMfFHeh7XPv8kJ7QejmYHOZYZIIAaXCkUCoVCobg4EaaFxYQHJM9VRa5mCSklTqFAXq9avwnDIHoRRUaeLBbnijZXFBL0R5txJwyyNc1gTZP/3C00jZDnsGHIZd2wZEuviywUJkWQnm/yJ00shJXAOm9cPGeIQjGPePksY4FFGKKuJo/XFTrdy15auUh/5IF7yf/068SLR3CEwz/+/udIrFkz7XaXJz0+uDpHJtAyvkl0x+XSD90x4zSEf3zzl/jLj3Zy2YldPLjqGjyhYTkuAWeMobDvE5B5/CGiVpQ1pQxPVW1ekY5DpugwXudqaDTNqeZlhlI5bNfD1NXcjWIK6uurf4+OztpmZT7HaKhuys/irsEGr5m8u4jC0cMcv2wL4OuqoZPmOKJy5az1CXzRc3hguGZZoFiiELT5xfJ+IiUHMMgbsHWg2sbRYMNID3/w+9+g5R//G+vwIGYkyit2/5gHl7+Uq49XI9DXjIDr2Fx76CjbdjxM9E8aEOLcbUkUCoVCoVAoFjLCNLEmRLBK16XoKIuA2UAWi+Q9mDidLwyjktV4MRA+SSzOlRxWFAMMRhvZMEwluGnbsT0VuzwAQwoOJIrYegiEpNEukpvlQld3fqzb/+PLnaf1upWuS/6k3YeCSmCdL5QKolDMAl4uy7DVUrMsb0KwUI7+FP5rxeEkRWkwGFjDqLWBd/77J9h9sKd2Y93d6OkspDMsT7okw+vZUhZfNClJZLJsXpyYcd+MtiVohsnidD+veu5nXH30aV63q4vm1COMq8Gu50AmA27t1Vk6pUqhK2mX+O9d97Cjfwc7+ncAsNiw0SbkWnulEv3pagVGOjurL8ULns43ZCqv2cTLZ0kGY9N+bkhBLPY6Hly5HEf4v9i0BQhYP+ILrSEHhEhQKE4RUno6pvmde2Oj9Hzvnppl9XVRdsYL6NJl8Vg3hucQLF8mmjMDbO7fw4v33s+Ldz6Iqfsz63sbwc5liRdz3Hx4qDLUvbQX4nmPy4/0cNNDP0AIQdcnRtm+4Sa2b7hpVkVshUKhUCgUioWEsAI1AitAqeSULZYU54IsFsh+5vP+82EmA+3tmJZFwLh45KPwSTUK7GIRV5iVACRP819ti5tq2omPfgxdFqtZcI4zrxGs0rEpTMwwRUWwzicXzxSE4qLibKrnzSdeJk3eqL34hmxqUnijJYiVqn/nDcCTvOs/P8yPP/CvBE3dv0AHBe1iJQDD/cMU4tvJlrMzmlKSy+/6V4wziBDVQmF+2pzlAwWBZWZoTI4QWbuFUfEM4VIfOWtRTfttff6gRAgX+aMf0fvN/2Z05084umYroyuXgV4Wx9oEK5uilIpphgJ+erZ0bHrG8ixNhFAopqLFsYh6Ol4hjxacnd+JzOdILVkJJf+M06XEBdYPw4FyMTdPQPfijpqU/HhhCN1rIG/655PrORw+McTG1VXvJSklr/jiLUhN8qN33DdtHzrbu/0/7uqsXLMKvT18tTWII2BPI3hCcssffoATX/8P7gmP8tH7kxTZhS1MAoUUf/grIT71vEnGsbE2XFmzfeFJegojXHnkCfY3b6cv1kzAsbnyg3/OpdvWIiZWCr1Aq7oqFAqFQqFQzBRhmlj/81VYfq2/oL0dz3FwPImpqwrq54Is5sma4cp7oetEg1btePMC5+QIVi+TJm81YNr+8rwBruPQ+q27a9qJYAjDLTDuxDWfAmvnXZ2ESpJVxzrY0H5dZXk4GJiX/iiUwKpQnBP1H/dTnq8faeHFIy8D1yVtSl9ZldT4po5TVwR3gj7aaAfYN5Bmy+I60g910dV+E8calgB+VF3MFuieJBZOENFc1jZPLuRzOozBUbQeh6ANBgKxVvAPH7ifO/7xTaxJLiJeAHAIeRCwBxmK+AW03Gyaw47GyJob6IlF/Rlhx0EIgbACLKsP8b/BAwxE1gFwmV3ieDLPHR82CUmNv9tj07HhRjwkmusgdHXJeSGz2AmyTIuBBv/w4Vv56VoTtzwAPpeJFO9P/4R0wybQtIrpfEv6OXa0XUKoHB26vjZTn5Tu8IdPHOJYq8dTi1pwNRAInjs+zMbViyndciN/vHYPLzp2CTc3rsd0bX7+6ZW8qPvwjPokXYf9u/fjaAE2lPddVxRsaI3R0dbB4JEHecdLbb7wPbhyrS+mPvyJ6b+DPU0gSkXEiUNcMjbIJUIgizbt7V88o+9KoVAoFAqF4qLAMLF6T0BDNTNKei5FR9mVnSuykCdrTQiEMEyigYukwFUZ0zLx3CJS+M+nXj5H1mogPsFCzChkCZ0U6aoFwxjehMLOjj2rFgHdfd24qbLdV9spGnZ2Qns3lqPjNE4IzBCCUDg4a/1RnBlK7VAsSKL3P0zcipBwDd4xfCP/9t5fzHeXpuW7X8gwsnIdR1pc0HVitlvWVyUZyy9Y090GugchO8/WgRB5wxdMAAqGYO/ew7Q+38+9//U9BhJLJu3jQKPg0itvYeO1HTP2Xj2ZfeVIvksLddDVhVksYDkjLB07RiqwDADLKTEYfJaD9TcDkssGYUcbrExGiZYgY417RUrqH+wi3v4rROMwEIoyVhjj/qe+Rc+zNtfKpYCk95KNfHFjG44eJPjh3+W9v/E3rFiz7KKa/VTMDOk4bC3GePl+OBFfwtH8GiJNdSTrD6JpuXPati0ga4agbPCe0z3W5vvJZGyGo+2An2a/fsj/DQugz8qy8dmnCLkF3EvGPY4lfaNZBo4eRx/uJXnpbTzXGkeX4Ggm3ZtexE2uNymCXCJZOuoQ83SOP74TbofiSzp5LrKc9Ql/EkYKWPrud1Iftnwx+fbyyrtPbZ/R0dZBIehXOP3rV0W58yfgZjJsz0RhVBWVUygUCoVC8cJEaBrWyUWD3XKhKxXAd054hbw/ti4jdIPIReS/Cr6nbCJfoGRUg5dyVhNe3v/bs23iubFJz60iEMTwqpZ40nVrI1gnWoadRVbZ1j1J9PEgrfvv92tYdHRMu62/+WGeH99chP4d0N6O0PRJ0bmK84f65hULDuk6XJ1PMFIWEkPPH8QdGUJvaDrNmvPDX98CW4zFrB6T6K4LUoLXS6wUQOCrmmPaKIuHurh5v8SxHb556XIy1jUACM/lxNe+yf+MjdHdCu19voUA+GKm7vkp+4H6erYsik/Zh9Px+O7rYcgXafCLIKIFgjwfynP7jic41DBC3gyxcvQI967MoOGwdsQAxw//G3cyipZ8oVgK2DrSjxYKk07tYU3fcrpbdaTn8JIewa6WVwDQWoClKT8SVw4afPcfv8Da3/ktbm1fiXURefgoTo83NkrY0xgK1/P4sqs40ADmAPRlEgwGHj/77aZTpE0Lb7x6ppScMArc+dAgn+ocxdFgLNxeHajgD4Tu2ilpztoEH7oX/TtfxRX+TO99z/2A/U+coH3lNZT06vkmBRRDMY4eH2D1itrp5OLBPVzR5HvArrbBGeijZ3iYQ0vaK+eOi2TLJasmH8AMBl5RK3rSgiis7TjtegqFQqFQKBQXM5r0MD0bW/PztaXnUXKnSCFUnBGyWCBrToiCvMgKXAGgG+heEagKrOlAIwCREpg5j431kwOfRCDAG6/5dR4vlC0UHIdcyZmbPkrpe+B2d0/+7MEH2X2FSzJcj+NNEHh1fZK/rOL8cZGdJYqLAafnODHPYAhB0WykZDVycN8h1l218ATWdVmLV6SXUgwsRvP8SDYhBFE5wqrhA4yFlqJ5Hs1tIW7/Tp7Fh8aIuzr/0QGadxmeZqF5ku5gkrU5WDvim2lnLLj6eNViwBRw6/bVZz8b1dVVnU2bIOhkDIGTTrPG3ouUHm6hgL6qjquPJXl0aRPtfX47AeRMcAU0pXppHDnGNj2CE42h4dGYHQJacYUvxo5Hu4Ivro7/LQtJ9j/xNMFwiFs21Xq/Ki5uvJwfbdkTX0Ks5Ed272qRXFeo56m6G7j5y7cghDxjqwBneIB0MgNlLVRISYEikd5BPvxfgj98U5pVuYM4xqWEnRUAXPrcLrbV+wNxM15HuHicdGgtHX1woDhK03CYsRAYZXXUFX6hrOgV17HnSH+NwOoVCxTjQaCIh8ByIfvT7/B88xoA9jX658SJVVE+2HSSUDpD3vv+DuDC8KNWKBQKhUKhOF8IT2K5TkVgxfUtAhTnhsznyTa1QXmSXxgG0YssKlIYBqYsUZhQNUUKza+TAtSXTOIf/7vJ61kBQqL6G5OeS34WLQI0CXooxOK0xpve3kpRK9GjnaAmHKWzk10rArx9ME5u0Tbf7q+QhB07MK+5hqCpApnmi4vrLFFcFKQPHWBP8y0sSkfxHI0DIfjAvd+lb9ed/OIdd59+A+cJKSUvP6iRDrRgyepFzCiVeNt9z/PQekFj9jBjukNP3SWAbxBOKMpHejfxe00nSAdXMx4fGnKoXNAB4sV+FqfSxAoZNg300tT66XPr8BSRcvfu7ODp9H387vVpAGIE6CxAa6YfaMLTYMsAZIXLjxcdRabv4ejrrqfr9scA0EZ9c8mNA338fHUrAHpZlGrv86NdvfJXo0nY2wA838VDw90sb3wfa1vOLiJXceHh5Xx/rOFI7UTJ2hFwtUUc2WshTzZKnQHyt3+LjLAq74XrEXCy7GsSrB+S7AtkWW2EWJd+kpfsfQocl0uOFhHdowBosTqidi+Z0DoksuLVKqlGbh9o8IVbvX8HVnMLBdslWJ4ZdnqPk7HC7Gu6mlygEcvJ0l0McaBpBQca/PNhNAgff9370ZQ1hkKhUCgUCsWs4QusdjWd3StbBCxwFnpBZ1nM0++WKJX88XvCMC8+iwDd8ItVTeDSwWoGp/Ak8eDkYxaBWoEVKckVipPanQ1SSm4oNGImAnRtvJZYvJUoECh1Vxt1duI+sxN3RTP9sW387+ZlZQs/0JNJ4kFd2fHNIxfXWaK4KDCGerG1EFJoCEBICNkSt7QST8oFI1J46TGink5PpJXGfHX58oHjMNTP7YdNpBAktl/NX7/9Pnh7tY3ee5zEh24kZy1iy0CQA75NI5EShDI5dsnv8qGDBkLTwHZpeOrA3B2IEPzgK76UFA2H+NNfM3nLzoP8dM0y8oYvgK4w4vzkI9+ZFEGrxeoAWJbsIVbcRjrg/9u45X8igS+y6p4vIOvSj2b1Ukn+/Msf4et/+pFJfpYAsvNmfm/xk8Q9k/6EyX9+tn9ujl1x3pC5LG/dE+bz7XVEynZZq5IQLE8stDiNPHW0gOtJdG3m57hEko4m/OJWuo6oq+dvfudDXP6l+yEMKWuMX/vpMIZpMYbgmuezNYMOLRon4IxgeDkgVJno8DS45nh10kMKyJQyOPkCB4aybC7bddjHj3D/0u2sG02wow2KRoQH73+MmKz2cWxRPZtaY2f1vS3EQbdCoVAoFArFQkBIX2AdR7qusgiYBUr5PLtXNVTeX2YYRK2LLO3cMDDcop+GD/DQQxVxFXyBtS5kTlpNWEGCQtYsKxZtHM+r1kp5sGzNV18Po6Mz7pI3Okyja/GVbUtoLvjBSwIIB7aRHkkSa0j4zz6L6vjl6mvJBBMVa8FxGmLhGe9PMfsogVWxoPAKeWQ+S8hJAs1oshxFlkxS1DwGM0VaYwujKp6XSiKBVNAXWD0NRNFmVWMd+YOgFfyrnWCyWKQlGghpsDi/A8O7Ah0dkIhCkZv2Pc0dN7gs2pdEGjr1WhgRObvU4tPS1cX2zs6qr8voKH2/XUekFOHDP7uXE3VtOCXBhqaGKe0JhGHwxofTRG2TN+/ay7NtG8gbsGEY6vMjXH5gNxsP7SfoSZ7efAm7Wq4si68SZzRNz1ie5Q2Rmm1K1yXTd4hLmuIUjSgNaYvk4UMkVk7hX6m4YPDyOXpjzWhSkDMgXB6/5CeMWzYXlrCvZ4SNSxtnvF0JpFqWgNR9b1IJ8aBZGcw8TjVKYCqxUovFCegmCbsHwZqaz4Tr8cbHuvjXWzcyFvI9mGQhz86eMTa2xtCQHDvaw1AoUT1OTRAt+oOu9j4Qms5bf+8PlNm8QqFQKBQKxSwzHsFawXUoOrOXrv1CJZsr+N6fZYRhXHRjWWGYvsBas1D4gqvwS1bHA5MFVnSdkCHY0b+jsuiKtZu49a6XI7Qid/Z1syVkMdYYoj8s2ZROocVmlrXpppJogSDp4GKaC35g0pYBAI3H3v1eXvQ/X8IRHkktSDKYmHIb6xZNvVxxfri4zhLFBY83OsxdP/44VnANmtcMVAWYREFyIpk/rwKr7LyZty59lJDUGKgP8v1ntuIKiSYF3mf+hde/9EP84J5nwHXREFjvvYNlly1j75r/YW+TL6xePkVqvhYKMxBw+KOHThAuJdnbsgYpLTYHTVYuTtDR1sHr/9VvO+cRbCf1b7+Vw0hruJEga3oOEx5MIa69btrVtdEUg3V1LEs+y+qRE2SsKIvT/dT1DxNIl29a11/Puuee4KaW9fxgfcJXxTTBsd7hSQKrc+IoScPiseUvwtHDSOC/H9zHVifKjWuaVMrDBYrMZXmmpZW9TYKcIdnU42BJg7BdjXg2POjetY/1SxpmHKkuheTH9S7Lkw7kx0gEEyROmm0+1TmkBfzrybKBfUTdVQj8meec5tHc8xyrn/gl4q9uIx9awZZeFznyBINPPsFzv/pyNq1bzvO5agS2BPJBHajOfuuvfwNbFtfN6FgUCoVCoVAoFDPnZIFVuheGRUB3X/d8d+GUZHNF2jPVAB/Lsi6+AsXGeJGrCeg6OA5C0zBdd0ovUyEEActCSA8pyp+7DlJaCIr8/bUul2breMVeKJkBnn3xdrY8sBthWpO2Vf/B8jNLLMroHaO85xtvZ50wSAXbJrV9vq6NS7NFLM1jMOynv4bLP/1cWdVbPnqEletVUNJ8ogRWxYLCHRmiZBd5074jFI0+jtavYEfbSgDirs6J0SyXLquf0z7Izptxdz+LFAInaLJiZRMlI059weUjq3PYbomSHuZdTzzKcSfgR80BQgrqQibxoMnlJ05/Y0+uX0Xrj/dielmuObyDWNrB6BsEYD6TgiOBKKF+PzrXRIB36mORnseyPYMgIF7sRVomSIm47vpqo64uXv/lm/ngl3uABOBH9h4bGIXNy2u2Zx8/zIMrr8DWw2wdgP0N8PST3+MXxx6g8dffw5ZlM49uVCwc3GyGX2xeg64FiAFtb30tiz/xJyzJ5Hl46dV+GwFDA6McGMyyrmVmUdvZ3n7sLR0TlghaYoEz6ls+l8YLmLzs2cfpXryZaAE2G4KrhEDG67DcIQLOKBDHTSUB+Nnnvso/tUJ7P0xMmNp2whdXJX5KT9PiNlrPsD8KhUKhUCgUitMjvvV/WE/ugVI5AMd1KSqLgHNCui7Zb34Lwn5xWKJRopGFkUE6mwjdwPQKU38oIVrMTRvYowUDGLKELfzvRboOSIs7P9bNj7eFcDWTR1ZcTl9sEfGSQ2TnblZf1l67kc5O1q4rB2UMjgEQLUo29rbycNtkmc4VGk8f6OPy3uMMLt5y8qdcc/hB6k8cQI+cnS2ZYnZQAqtiQfFX//cHLPYksVKGeClDzrIIOSsBKBgGvb3DlC5ZPGczaNLzyO3bxb/dECBrNdCS38Sh5lZc4QspBxK+b2TIhbt+voP4VTdBu3+xFK2LWZqYuefJN999P2N/puMFTLSijZFbIOks2zvIW/cDYIZmFnlXzvoHIRDhckTqSZGxXW+7j0OfbK/4xGTDOiNjOTJFh2jZNN1rbGD/qtUMrr0aUd5kR1954/2jPPLLp1n3hk4CxoXpAbTQDe3nkpFcEVurCo3CMPnnX43xjTuexrrmEkpGHIHAy2fZcWyENc2RGUWxDsR9f6gDDYChc82tr5vSL+lUHLByXOLWsSR1giWpE9T3Z7BGUn4/gZTpUl/Yi+ZdBsCuFr84lyZBmzCG397rsGngKZ5v2Y6tmwRtyXWXLFNR1wqFQqFQKBRzgLAsrAl+mNJ1LogI1rUHx+a7C9Mii3myRhDc8rNpJkMkfBEKrIaBTqlsB3DSZ1ISKeamX9cqC6yMC6wu8qlncVNjaNoaDjbdyG7DL7wmMIgeGGTlpZNryXz2++N/Sfg8REuSI/UrKp9HSnAoUd4nSWK9w2y3HYYiDZViunsCWTqffxSzfzeuo5455hslsCoWFA8W9vHdh21kwMQyTDLmIAXDQ6LheS722CjHkjnWNM2NJ6lz7BBOOMCh+kZ6627gqhMa0ZLvfyLw/7+/gUql8czjD3FgeZT21nZEIMDqxjMzlT64YtyPJcT2WT2Ss6fr9i74cufpG54FLY11COHgCl8Ak8UCJ5J5NpQLABVCGo+sWIXEv6GMi7EPL/X/L3Z8n0u2rOfak6JeFQsb6Tj0nDRBHI+G6HrbD+j++wZWjD7HvmY/ijXz2IMMPrWTA317WPfdr516u6Ui6VCU9v7yYKIuSkPdzITZifSaJU5Ek+ztDNBjFnnkk7UDqhHT5ZM/PMb3N20kE4iQscAtz/F4mi+yap7H1r7dLMocpy3dx2C4kcu+3UU8ETqjvigUCoVCoVAoZoawgjUCK86FYRGwUBgP/rjzY9289zZ/WcOGbfx2phHi1e81FrkIx7O6QToqy/ZeVAVlqESwToewApiyRKXOteuQNwIIQ+d44nI2DofY1VL+SIN0Js/RkRwrGyPTbRIpJcGiwWho6mxdCeRGkxxuXEYyGKe9z19+Zc5i8eggkZL/XKKYX5TAqlhQ7I6V2G1lQML1i67jP17mYvYNkrdaEYCXzdCXKsyZwGr/9u3YmsFg7HKCrlYpT7VlAOIFX/BzNdhbLqoohFtJD45EwrTGz2x2773v76j8vaDiGafwjZ2O7Wuvh0y3/2aCGfpUGP/yL9j/9mFy1hLwXGSxwPGywOp23kwmFmUw2kzI9r9zAegelWjWzT0OOz/5BdYf3UHTj793lgenON/IfI5ht/Z2s7QphhCC976/g0VP7EaTabb0xZDS4fH4CPevbeD47wR48IvFabYK7ugwWTPk+yUBdGwnnjjztJjoVdfD092MWvDIJyYPpoYsF10Krjr6FPetuRZX6ETK4n/e8AdEm08Msm3no+h1MYS02bg2jtGQOOO+KBQKhUKhUChmhrACWKIqqEpPWQScCVN5wUaLkrFgrBLZKaJR6sOT/UMvdIRhoMkSjmuDELgCdFk+ZilPKbBqgSCGrD6jyLvvxmoN860bl5IKNsKYH5gFvnbQffRRVp/YfmqBNZ8lNVggXNa1U0FI60WaiiWKhv9846XHeGDl5X4hLvxiumZLPfEnRs7lq1DMIkrjViwoRu8Y5YYDNjccsBFd99Eb0wjYvi+pRPL0kUfoGcnO2f5dIelespmNwxF0zxf12vv84jueBlsHfMEP4b8k1Zm9S5Y3nXHk3EXH9df71dvLFdxPRosliLuj6JqOrunIUokTyTxSShzN41hdG57QyFqQtcBKpnn57nsJl/yBUrQE+dQIj2YvTIsA8GeI7/xYN3TOTZTwQsTL50j+8nFfgM9k8AQ0xvxo767bu2hYto61Q88SK8F4EELY01lmTV9cDcAdHSFjVaPGtWCQWOgs/U63d/ivKfjZwxsIFVyWjQ3ylu7v05b5JVt7dyOkh+WUWHzsGV666xc0iBB1KYd42sG49/6z64dCoVAoFAqFYkaIQACLiRGsDsULIYJV04lioWnz90zTeVcnmVKGTCmDmxojk/df4ZJkLBD1Axh0HXHDTSTCZ2a/dUFgGCyKtmHIIgJBzsQ/ZsOAN7yB6D0/mnZVEQxhyQnpeUKwzV3M8aZXTLvOsb4RMkXHfwbs7ITuboSuY0YiGKEQb/3SK9jaX0/I8Z95dU9SdHqJ5w/jauBpAuk4FXEVQMTirH3/HyOExNNe4DrEAkEJrIoFTTIosLwR6gpQVwCRTDIynKRgz75f6a1fupm9xTGeaVnti6j4Il8qCDcdvI8bD/6IW/Z/j7c//SBCStYPwfohX4ANWAabl76Aiy91dPiv00S+arE4IWeg8l7aJfIlm6FsCWfvbo7UL6n8W9cHEmj5Y6RSB2nK7KlUmpeaxrGmRRxP5qfZi2Kh4b7lzaSK1XPW0aAhUp0J/8Tbv0FdsZ94oXbyJGiuw/Uk0+GlRhnTQ+A4fsXPYIh4cPYTMzQE0WNDxI4P03yon4Pb42zf+yShoa9wn/Vl/vPtGsHB/urkwjQTDAqFQqFQKBSK2cOPYJ3gweq5FOfgOXE2ka7DluAirtEWc52+lFd//iY67+qsqdVwvnBdF83xyFqCRU6AoNQYHOylJEV1fG0FqD/D+gYXAsLwnxn0ciRq2Ma3CXBdhGESsaYXv0UgyBvXv4T21nbaW9txTZ2i1UbehLWj+GItVUuxTCmDl88xmKlGvbqGRrixmUVGnMVWgr/9ylEygWqWrvQ8fu3xAeoKPWQsSFuSHf07avvxohexpmn6qFjF+UdZBCgWNJ4mSIlBgtJFCh0JuLkM/ekiKxrOzO/0dNQVJWNaM6KcGrB1AB5qLWCM/YDOvhE+fqOJLTyWrl/O6+7dyYFGv7iVLj1ubNEJneIiPB0vtEJHWiSG6eUx3Qy27t9A3JEhjgzUsSQepyfeWmmrv/q13Pr5faxdeTX/IZ5H91byTMu4/49O2/4+ll6+ah6O4uy582PdrD6W4W1v0OgN3E/ors6L/zfQ2Ulq7z7clq3+QM0wcDRBIlQVWPWmVt49sIK96Wf5/qYrK8ulMBlMZmlrmNoSpJRMkgpUBxUiEDyrFKaZ/BsIoWHYHkSj/Ogd98FXOvl0T/nDi/3fUKFQKBQKhWIBcrLAipSUSjZSygVbZLS051k297oMhR0soP75Q6Q71pz3fvzsfbvYtbQZaZgMrdrAKjPCtsJhxoq1RY6tULBSkPhiQph+1pvuFeGkx3hhGKc8ZhEMUa9NEPJdl44+6G7zs12l8AXbdDmxzvEcZD7HSLbEqu5uAEpBAZpguPx425AskllRtTqTnkc4l0TILKYzRtipY01P0q8rDSAEixNhFtcFORi8cLM7LzbO25nyxS9+kZ/+9Kd8+tOfpqWlZdp2+Xyeb33rWzzyyCMMDw9TX1/PTTfdxOte9zpMc/ZnTvr6+vjGN77Bzp07yeVyLFmyhFe96lXccMMNp1zv4MGD/O///i+7d++mVCqxatUq3vCGN9DR0THrfXwh03V7F696bgkNqRH6Y80AfKf7f7hs+6ZZF1gTecmxxBLAr0i+esjjVU88z+3GRjr/qtruGy/7Kvu+fjnNmWFSgThNPVlW/Oats9qXC4oz8GsVpklO9wjb/Ww8FoSjD+EAO/uuwa1fgifK03xCEIjXseJbdyMMjf3vX8zSzG5c49KKn83nv3snt2y5k3jwwplRLQmXwrJmbspD0nH59+M757tLc45E8vlbFqEJWDMC0XCU8OU3ETSrCRRCCMI/uY8NS5v52dqtQNVI/0TfcFVgnWCrIO+9l76RVPU3A+jBEI2R8+QRdQa/e4VCoVAoFArF7DNJYMUvrmq7EstYoALrwT38znMB7trkG/o7hsdPTuygqE+ftTXbSCnJN0TQdJt/v3Q7yfAKMha09y2nJT1e/QJfxGuILlix+pwwDDxRFlgnfWaeVmCt01xMIbG7d1bS9icWwr1mf5pHVveRCq0DwMtlGc6VKtuwI37tlu+v99+/dJ+ORhjhQUcfGAW4um0D9+kPEy8cwDEundSPS5Y1IoTghuXXn803oJgDzovA+u1vf5uf/vSnp22XzWb50Ic+xJEjRwD/oXtwcJBvfvOb7N27l7/8y79E02bP1eDQoUN86EMfIp/PV/Z3+PBhPvWpT9Hf388b3/jGKdfr7u7m4x//OG650pwQgj179vDRj36Ud73rXdx8882z1kcF9Acc/mjPMHdv8wVWx9ToGZv99PD6nE4ymAD8tH+9rpFbv/dTSIRqClBJKcH1WDuSBJIAmKvWzXp/LlZShkeidIJnWlYwFnAAMA4+SGbptkobDcHK1joswz/fd8QLrB86RC68GVf4U4FhG04MJIkvbz7/B3EWdN7VyaetEI6tc6QugaDIZQMXcSp5WQwtHdjDiU3ryZXrvwmZ5JWx0KSBmtANwv1jDIR6cMUaECAQHB1IctklKyZt3kuP0fd/PwRRrrQpBE31MUxdOd8oFAqFQqFQvCAwDKwJY7+Hjj3Ec6P38pePf4/hO47PY8emR5Z8kU0iGIysYShSR7O9n+N6z2nWnD285AiupTMSitGaW04y5Ede1hUga1WrjAhgaWvDeevX+UQIwW9d9Xu876Gvs6YfNA/GheVQ0MI4xTOFFgyhCWjQXfqnKPJcyGb4lV/+L0+u2FpZ1n30UZo2bmJ8L1IT/GC9JG/W0Rtv57tWEwII+Y/HmLpO4gffIfvPNxHZc5C0vpU9jSYd/eXPPY/lKxf7b1Tgx4Jhzp9Ev//973P33XfPqO0nP/lJjhw5QiAQ4N3vfjdf+cpX+OxnP8u2bdvYuXMn3//+92etX9lslo9+9KPk83laW1v58Ic/zN13381HPvIRmpub+cY3vsG+ffsmrdfX18c//dM/4bouq1ev5h/+4R+4++67+cAHPkA4HObf//3fGRgYmGKPirPlkfftY+nYcOW9KQWDI2mcWawQ6RUKRHJxPM0vZgVgvfrVtMWDk9oKISg5NsL1EBKMVA5j0dJZ68vFTtJ0CbhJAk6a9n5o74ct/bUztuL1b6yJUN6wpJ2f140SLQ5WlhlCp7fnAjnXOjsxn9zBWKSRH258CSfqb+J4/UsIWy9jrK9/vnt35oybs5+mUJdEUjAFo+GNrB+m8mpsSEy9Ql0dv7VjGF364ipA72iWTNFBug7vWPoET2aP8b41Gn/+Z2/iebd6fgpNY1HLNNtVKBQKhUKhUFx0CCGwLAtzQhSrIUFjdjMdZwspJbJYQAKH6i/leN0W0oFlLC/exNJkG40fqz/9RkyTdFAjHTx7Kcc+doihUIJHl1+PZProVCEly9pm0KcLFGFa6F5p0vJIeLIGULNeMAh33cXTT30V13PobvOjV0M2kCtw464fYRSKeF6ysk6mlCGZHMPWDKQmSAWjPLPo9RxofhG5QNOkfcTifuDIZ/7gHnKmx4v3H2ZikPPKwWOEQqFJ6ynmlzkTWIvFIp/4xCf4z//8TxoaTj/r8fTTT9Nd9qMYjwI1DIPGxkbe+973EolE+PrXv05mihmCs+H//u//SCaTBAIBPvjBD7Jx40Y0TWPt2rX8wR/8AVJKvvzlL09a7+6776ZYLFJfX88HP/hBVqxYgaZptLe38/a3v51ischXv/rVWemjwkdEYzQ3RpFatTqem8vQl54inP8s6Lyrk3d9+sWMjUzcqWDRklb0aarxved9Wzmc7edwpo+DpC7OtIk5IrZkDW95skhzZi8C345Bk1TrfwpBsC7OskTtwOhES4jX76iKkY7n0DeSPm/9PlfqU0V2tW0jZ1ZvhCW9jgd3HJjHXs0CpxBaXSF5evlmIo6JJ8AT/r/zopVLpt3c0pFehLTJB3XyQR1ZLLB/ME3+oXtZ4y3j3vUv4kjDJo4l1lMwqt+lFo2zuG6OBhldXRCN+i9lA6NQKBQKhUKxYNACFhGtGnhjSIGG79E/XjxqygJSMwwYmE1kIQ+ey+4NV5AML2fdiB81GnE0Nrkv4g29zUjbnn59KfndNwf5l9c08ye/dRm/+e5Obvvcr+DJM7MXcIcHeaTtElytmtAcsqvp7eNBR42hwFnVN7hQEIEgljuG5slKkBWGQWPdqQV6EQjSl+kjI/sq9nXgB5dEx/qpTw8ggObWZpD+b9P1XGQ+z0gkTncsx8/WXMn2XhCSCQ/CZTyPhlIO8KNl/7t1mOVHH0d4h9E8l5b0IDc++/CsfAeK2WXOBNZvfOMb/PKXv2TdunV89KMfPW37e+65B4BVq1Zx7bXX1nwWDod50YteRKlU4qmnnjrnvrmuy89//nMAbr31VlpbW2s+37RpE2vXrmXfvn0MDQ1VlqdSKR599FEAXvOa1xCN1hZeue6666ivr+fJJ5/EPsWFUXFmCCEI/eUHcI0s+bKBs5fLcmg4e5o1Z8hT3ciDI6SCy8gbkDdAjydY1hQ/5WpZS5A1qfRJMTOO12m4js27Hj3CjQefpaB7RItU5051jes2r6jYA4DvxTt6xyjLs2l0SSXCMZnOU3JmL5J5rpDd3cQDdQxHGgGIlvyXrukcHcnheufPc2m28JAU9u/mjmX9vGPVMC/97VjN5xJJT8hkV+sG33LD819eKsmKxtg0WwXLtokXTlS3Uyqxe/8Jxt79Do4nLsfwqp677oQ7mOHJKSPOZ43RUf+lUnAUCoVCoVAoFgzCChAV/vNAKO9Qn7JZ3gvU18MDD/qvqejurr5OwSlF2jPEy2bI3PkvPGW1kjMk7kkxOmORl7P/mT3Trl/avZONpRhLC9diB65CBDaTONTGPQ/u9G3sZnAMUkoGB4YYDiWIliDs+M9WebMchVlOUQ9lM3Te+eGLOpBImBYhZ4jWTIaqRi/ZsChx6vV0A1koECn1sChVDfgZ1ZL819pfct3vGbz6M9fzN6/+e0KUKpl5Xj7HE20hfnD9SnpidXjaZG01b0BW92j+yN9WlpU0yY1vSvPL0L289eGvcdvjPyQ2MIxi4TFnHqxCCN785jfz2te+Fl0/vQD1/PPPA3DNNddM+fn27dv57ne/S3d3NzfeeOM59e3YsWNks9lT7q+jo4P9+/fT3d3NrbfeWumj53nTrqfrOtu2beP+++9n9+7dbNu2bVIbxdmhN7Xw6meP8b0Nvqj90N6fUr9q9blvuL6eTzTa/NuLt7CpnG2uS0Hg9jezoXV6Eajr9i7q+/x0iY62DpTkMnM8TTCWHGL5WIDLRp5iyYZ+4oUrSAVjCOASN82GxYkp121MjxIrSSSCbFjHs22Gs0UWzVXk4ixheyUichlBx7+9CiBtgee5/OLg/fxK7gaaooH57uaMcZ54lOyien7QcTmDDSsBaPM2YxeKmEH/OH5/2Q7i5mUsylbFc7dU4pIjjxOypr8naI5HIn+MHtYC4GXTDP7P//C1ra+iYIrKIGTrgC/YAiAEl/757xM01WSHQqFQKBQKxQsJEQgS1dKAPwmfseBEKMLTUT/zVUfwZG/3lOuOt9l+qh08XV53e8c591XmsjzXvAZXaAg8oiVwBRWhtb3H4Z7PfQ27/yNc8u3arFgpJcU/fjcjS1aTD1azwYK2ZO9z+2lobuDKjcv8hScVhm24/1EaXJN+y8F7fYqD/3cPUFcJVrjsaJ5rnvs2y7IeeJCSARJjIzTWf/mcj3khIwIBBJKbDj7MrtbN5K0w680SSxKnt5hwSiUczeK2PfdyPLaIGw8V+HFgN7rmErXq6Lq9i9Lh/QhnDGn6QSBPHPgFixvayJs5Ii7kynEja0f94JvxZ6ZgrJGNE7SIqOVrIM+HkzxPHgJwY11iNr8KxSwxZwLrm9/8ZgxjZptPpVIVwXP9+vVTtlmxwi9ycuLEiSk/PxN6e3sBMAyDNWvWnHJ/PT1Vs+m+vj4AWlpaqK+f2otkYj/PVGAdHj79LEQikZiRYH2xoTc0szzZi5C+MXTIhnQmR6pgn1MVeTtkkmpbRjbQTL78c405GlvXLDqtWDN6x0VcoGiOERL0bAGbAr/+mMGKws9IBuuwPJu25sXTzpRaP7+X0uc/Rl6Lguci7RKDmdK5CawT04LmKDqxVBemaFSvGWtGoLsNNE+Sz6cYzpYuKIG10BDlU1c2Io2VrC1ba0jRyHO79tB+xTbc5AirSmGebFlMS74qhK7o38vqwil8c0dH0ZIjFP7iciw3hWaHuf/w/bQXICZ8cTVbTsOJF6qDkEvdES5f2zZXh6tQKBQKhUKhWKAIK0B0gkXA0lKAOtqw6uq4yvMHjs0nHKTjICboEze9MUPCMxjSS1h3ddJ1++TnANl5M4mVBdxwgN7uR7lF3sy9b7vvrPvq5TKciPvZsxJ/jLxhGPY2ghR+JKmbTfOIHWa17dY8j3qpMYqDgwxsvIEl5aDJ8fT0sYe7ePypbhp6nmf5pz/OoVSa3kQLDdkkKzau5qpLF9MX34xuxnjvHb9Luq2JrePBRa5k075nYGyE3RZ0DEDLWR/hhYUw/eevukKa6488QkBq6J/41IzWdfJ5wML0XBL548SPj3LnQZfLc3V+1hug19UTkkXy5acWPwszQsE0CZWjRnQJkRLkLEnIljRnhrkhfZTYBI1jXHfovKuTWLjbX7i645yPXzH7zJnAOlNxFSCdroZVL168eMo20WgUXddnpYDU+P5aWlqmFSvr6uoAavY3vt6iRYum3XY8Hp+03kx517veddo2n/3sZ2lsbDzjbV/o6A3NxIspdK9IOhCAXJJ7Hvlvbt7052ctsErXZawpzn9t72DrgF85UAB14SCXLb94zbznm4nRvwDf/ewoshglns9Tl7Exd00uLjeOFo6g20PIgO+rdN/+n7L52ivPui9uSxN/8lITKQLssk5w31lvaXqklLghq0ZgnYjuegxni8D0EdMLCS+dwg2YLMquJxXy01j2NvqDxD//7j/zo8u/hH38MOnwGmyjarOxu8Hjp+ED8LZTR3xrsTjvv/4O9pQCfGbnExUVNWfC2pFy1U3HZvVIL5r0WJnsYf23vnVRpy8pFAqFQqFQKKZGWBZ1mgv4dR0A4rKFhAzjCpOSHqXRheKeZwhu7gB8D9LXjDWhISgKj4fzky3HvEKebN8hti/dyHGtnUYZwnn8CM5veKesMH8qcskkKT0Irt9fV4NL+p9Fspm9jbC3ARAOQ4s0rhvJsfYtr8TRJEKC/Nxnea55DY4RBmQ10qBc42CklOSHdUvgo1+CdVf7nzUDqwRPLPO/mJADpfKhjgcX1RVcti1poel4AjZ24By/n3xQJ2bV2iFejIhAgLZoG3Ckuiw4s8Adp5DnJ/WSN+smlz9d4JIjJfIhAyZ4smrROKZXa2uYDyfIBKB+Qm2t9p5nackcpDHnYUiXprapgw67bu+C22d8eIp5YM4E1jNhol9pODx9OHY4HCadTmPbNqZ59lGLjuMbi0QikWnbjH82OlqNUhzv56nWG/dlnbie4tzRgkHcYpFIaYh0oJwSkU5xYiR7ylT+U1E6uJeu1VdTLItAngZmrsTln/gIhjZn9sQKfFuFcaLugxwTGXCguf2mU64nwlFWBAM8K8dnAQWDqfwZ71923swfLH6al2y/lLHGzQBssPspZTJY0dkdTMh8FoSgaNaTtfwZysjEYpVSMpTMAJOrRy5E7N5jjAbj9MfaKh5NEkBASMY5NJhG7tlPT2xLZR1Xg1c/0cMbHjiKdhohVOgGd3XfxbUP9ME1LwU0oiUolO9WextL/CjxPT7XnQLLJJ62MZet/P/bu/P4uOp6/+Pvc2ZJJpnsaZI2SZM26b7TjRYoFhQVkboii8vF7eL13p8gKgiIKF5QFK8XfwJevdcFL+6/C+oFRbuBylJKN2ihe7olTbNvk9nO+f0xSZplZpJM0k6W19NHH5I5c875nO17vucz3/P9notNBQAAwBhnuFNU4AjJNM7mHG3D1K+WXK3D3dVrQ/rj7uO6au5iOR2m/Ptfk9n17RTb1MLT4QHLDbzjCh3JLFRV7grZhhl5i8o9U+/+98/qiZv/TQ7T0PqbuxpQLFsatQVsH+vX63RGppRW2vOR0wppb/Y+veu1g/r2xW9TyIy0qMwNu3TwjcP63ykH1eQplc+dow/ddodezy7VkmpbbW5p9hlLVlei1xuQHJYtKaS2jiZ5Je0qitTRKxojydXu7gDCXS1luxXWn1ae2RYZyHXzZjnXrx8nzT5GznBH9ndjeuRcKAq4ZaYP7VlwRUeWpjS26bYLg9IlF2vzzTsH7DfD5VJYbbK7OjlrC7SpLi1HIUeo5zszmqRcX5PcVlCtqZJl2ZqyiQ4Ix6sxkWA1u5JZhmHI7Y49Sl13q9hAIDCiBGv3+oa6rpHON1SPPPLIoN/Jzs4e9nInikB7u9L9Z7TQ193nTFjHq07Knls4rNZrdjgs38t/15d/+m1NSyuOdOrtjPSHOaOxVnMKJsstJXl6V0B2fD1HamvrmhD/ZmKmpctjtUrG2VbkjS3tCoSsPoNiDSZghpXimardUxf0fNbpLtT2ba9qzfoLh7ycobDa2hQ0HVp4Jk2poUi3AIbOnq+GDNU3d4zqOs+lcF2t9i5aK08oct14ujrGDxtSimXouf95Ss1/3Sg7L0XZndJrBdLKjlyt/v0DgyZXu71gHddbAmFdceConpgf6Ws5NSSlhUL6+jX/oJ+s+Q9p8/kb8RUAAABjk+FOlduwlW+GBkzrTiSGDelYs1+V37xazcbf9O6jbs3t9SZrQZsly98pMyXSV6bV3qbm+lr9bdabJCPyjNHdsCDlRFC7jjfogn94n9pmNUU+/PvWIbUs/I8Sv0q7Zrn4mFTqcuvdJ+bJlx3SJ17eo/9YuSLSONUwdOi1/dpf8BY1pEUSfk+GDGX5IhvkDUjOaz+oq9Oa9edHfqiWlKlqc0c+D3cNnFTR0FVPtyKflzdJh3Kl2fWR/z+YKwVtn/7xkV/KSO+V35hEA7qaGQMHtR5qgnWAGA3sCjI9OhMnNWTalgqbavWrJZG/96+s1MO8mTdujYkEa0pK5JcDc5BWg93T/X5/3Fakg+lOkMbry7Q7Ydc7Udod53DnG6rJ+Or/cCxrTNHjoZMyzKWRDwxDbc0tauwIKrfrpjDnq/ma0eFWg8vS3+4+IZer7yluh8Nqf+s6vZQ/U/VFxSqxpCU1kV873W0dumr/djlMCrTz6bO3L+3578Fu54bLJdNol+ywbMNUyApJwYDq2v2aNsR+WG3Lkr/muJoWLFF2i3p+6u5MdWrHsXrNaOkc1dHorY42taR4lR6IrMgIR17pSQtFyjNfqkOdvk519utnaczI6ZUADwbV1tCgw+FUOQxDnpCtF8qdCluRX/3XHg3p1Im/an9+Ss8rWpZsLb39/2h6zuCdxXd7JTPSp9EJ7y7ltncouyNTmR2NmnVyn4pWPBT50iSq/AEAACA6IzVSby/51Y+1JH+W2tyRxGKvblkjjRtsW1lWqdrNPGWHfFpeXaQjudNl2qZWL3yHrMZ6mUWRhjz+q9+uHXl5CjoijboW1Uo7I12nyjQMvbzvmKalZsptGQoZtqwhPD7uadynTqPX23qGocLOSBeEqZZDJc3H1O6yZBumwob0bNWzWtvg1a6udmVmuO9481MK8jR1wTy99TOf1Itly7SvYPrZVqqm9EZeJJmaForE73eE5XMakmGqssFWYfMpve+nP1VWeuzGYxOdI3fgG4SOKUMf12Hzb7uSsT+M/Vxy22W366anf62AI3ojrqIzh5ReVa2P1KbIsC1l/XLkYw4hecZEgrX7tfpwOKyWlpaefkz7a+t+yB+l9cV7jb970C3bPluQJTofRk9j+IyKwh3aXRjpeyZ8YKMuabpAueluBS5bp48VeVSbsUAncmfph4//SRdcuFSrZp8dNCl47LC+V9io7cWRc6z7JuQK2brqJ/9XWXm0Xj3fBn2dph+/y5bL16KAM1uSZAeDOtMWGHKCNVx3WkdziiWzNDL4Uq9LNeTzadO+ar1/RZlcifStFGXALLvTp+YZsyWnUwqFZNi20gMdMux02V2/iNvBgJp8QRWNxQRrW5s2XCdlWS41/Eue7ljxHVnOSE3PISnDlaZWX63CpkeGItdUh0s9HefPrmvSqhnD+/HodGpYHXVn9LZ2j+5Z+6Iqd3RKkizLKcM1eSuBAAAA6Mv0Rp7fZjYe1668Cr2RZ2rJ6UgbioxApCXnG3m22p7fqqKikDIDpWpz+/T38hWSJG/Q0MaODHUcPq0v/PEGSdKvjh1Q9cKLetbR3V/p7HpJhtT5xG/1m+mLtVIhuUPNqg3uk21ZMmI0GAs31MkwXZrektsr8Wtrqq9FUiQBXFTXIW/nMZW3lutgrlTRcPYhxedST1+rkuQwTC2YMVWOrEyl1jVorudlef01OpU5TbnVDZpae0RbV9XpijcK1OLJlscf1L8va9ROu0Hf25wmR2dQ2S1BmVmT+9nXTPPqePbZY+ZqD8iRXzi66/CkyfTXKNTVt2tr1+NeekDKaGnUhq0bZdi2HB2R5x3GlRjfxkSCNT09XR6PRz6fT7W1tVETrJ2dnfL7/ZKk1NSRtS7Lz4/8UhFvIKqmpqYB60p0PoySxkY9eqZGj3/iE5LKJEmBjjZteOxDarOf06snU7VnyRqZxjQtOmWr4eTTeun555Vxx82aPz1y7AJvu1x16xZLMhTuKruMUFiXvLFTJSRXx4XPXnaXPv277ynozJHDdMgOBVTf5h/y/L4jB/VS0TxFvXXZtpobmvTG6VwtnJal9T9ZL9m2HJb0l49uGXTZtmwFD+xTc36W6n/yn8pdtlKZoQ41ubta3BuGDIdTuZ40dTrbFXRkSFZYCgXV7AuOasvZ0RLI8Ogyn1edzkzVWgv18vNvyA5H3pEyHE6FUmr1sWe3a9v0iyIJa0Vew7IlGZalda+9NOSuAbotLVqq2bVbJbWpca204Z+yez6n3SoAAAC6mV2v0KfnZWt1zWt6pWiBpEjSbHrDNjW6vfK55ilsSkWhFBnGAtVnSCVdYw85MiKDW+/80a/17y/sU4rLqTeK5qg6Y6rSA9LiGskOheTMCWp/XiRJ5rSbtKTOqaYpMyRJ7lClTr78skpWRR98N3Bwnxq8eUoJn21M4bANTXviV5LT0fW3rZTPLNCR7GLJjjRmaHNLlY2RwbsOdXX3apmG3n3lhzS3KPLsmrtwlbJefF6zzEY5VqbJ2PystH69rjhaKftvf5b1lstlphpa+/Berf/Jel0T3CnJpc3fGZ3Ga+Pdd8Iv6t3NkWMwu8Ez6glOw5Om9OBpVTTN0KsF6mntbFiWSo8dVkooHGmII02KgcUmujGRYJWkmTNn6rXXXtPhw4dVWVk5YPr+/fslSR6PJ+5AWENRWloqt9stv9+vkydPqri4eMB3DhyIjGTe+7X9iooKSVJ1dbV8Pp88noEt5rrj5HX/c8ORm6/D8xxq7x7kSFJGuEilHR7tnTpbftc0SZKna9w0u7lZu149qHmlebJam9WWnqbGtFKlBSOt7P6nIihND+iWXz2fpC3CcJlp6Uq3O+QzIzdCOxBQfcfQuuSwQyF943cPKs9dooW1kT6JDMNQRYOtg0ZI2w9sVW31c8or/IIWTM3Ud771mor9piyXUx2PXyzPn7bKiNJFiJ2To2C6W1VTCvT3FW/TsewMafMOafMOrVu3SrWXv0Pa/prU1iYjFFKOv12ucFeCVZFWuM2dwQHLTTbbttWZ61WHK0UHp1ysmU2p2pnTpMquLmMzwra+suFz+sWey1TSdFxNnlIdyY5M21rs0zH7WX3pyIHEVt61nzc/4Y3ZpxEAAAAmN8OTJjMtXZakWY3HNff0SS2szVK2r1FZwYD+a5VXtjlX6QFDc+oi87yRLzlNp/Znh+Uw26TTu7SkrU2PrV6oprTpml+XLm/344Uh/fZN+br8hZ+pNnOVKpoK9GqB1GaG1NH1+n6Hy6vf7zqu989dpPzMgTmCUPUJHc6N5BKsrgaTU2+5RSnOs88VhmGo3t2iaS3PqS31Yh3NdsuQtGTOpVr1swfVNsejDneuWrw+XXnRwrMNGDZv1oCnk6436QxJjs1bz37MKPQDhO2wQoGuxiMa2huRw2F60pUSOC3TtuQJmjKtSGMUhS2V1R6VLSlz1cWjvl4kx5gZKn3x4sWSpL///e9Rp+/Zs0eSNGPGjBGvy+Vyad68eZKkv/3tb0NeX35+vqZOnapwOKwXX3xxwDyWZem1116TFEkYY/QZDqc+veYGdb8gEbbCSrNc+pfts/Rq4XxldN0IXy2I/NtZaKu+rlH17QGFak7qQG6ZFtc6dUF1JAlr2ZYcTvo5GU9Mb6bcoZaev22/X02+oEKWFWeuCN/enWpqy5Zpne2XKZwvXb33OTmsyOs/6QFbv3zm+zp5vEZ/WuTWrqnF+uqlF+pLlVN047ffFXW5QYX08BqnNlWsVENaJGl6sKvz+Iff2KbToa7fsrxeKTdXUz/3GTmts78a28FIC9axxu70yTYN1adXyjZSe34575aRmqKSijKFZWnFiZc1r/YNpQablNp+WOHKUzpwX4LJVUkHp3t1cLo3MqIpAAAAEIVhGHLPWSTV1EiSUuxOZZ16Xf6aKnWcPq1qs1Hl9XvlsKT9eZF/sqX92eHIq/dd3QDKtlWbMU/lTek9b2VJkpmZrUff9wXtTDmj8oZtSgn51eaODMDqDUS6IfAGpGDI0ov7jg2Iz+poU/2/fl0tnmlyWJGWq56OsCqmDWyQ9ZrXr5RQk+7YslHv2ntE79x3SlduuFxzX92hz/1ll+5+eot+cNt/yRWloRfGJiMtTYYsTW2p7vlsdr0UstrV5jitQwVjps0jRsGYSbBecsklMk1Te/fu1SuvvNJnWktLizZu3ChJuuCCC0ZlfZdeGulg+umnn1ZDQ0Ofaa+++mpPC9b+6+ue7ze/+Y06Ozv7TNuyZYsaGxtlmqaWLFkyKnFioPS8PLlCTbJla3GNrbmNpp6eu0iWYcqWlBaM/CrU5o782/r6H3XgdIsCtaf1RlaJ1NU/7qqOHP3rR+7TlhufSe4GYVjMzGylhFu0+GRIqZ0hbdv5v/rxjh+rqSN+gjK8/k3acfMX5TO8sszIr8dmKKz6rNMqbD0jv6NNnlAkwZpxulEv3flVtboKtbt4jTrcRWpKLZblW6wTjR0Dlh3ISNUZb6na3XFa1y9ZIi1ZIseGDZqWnyFXuE121/9ePPqcWjoHjnyabFZTgwIOlxrSz/5gtKg2MnCA129r1Zc/L0eqRy+lNssIh1Xa9JrczU+pZnmHDLMzzpIBAACA0eGet1ipjR1ytQf0qWcDWj11lS4pX6dllRdr+xRLuW1vaFHNa7IltbqlisZIH6cXnXRoSeESLSlc0vOa9v68yJgC3WN1VL777ZpflKn/ObxKf/fUaNnRZ5QSOps/CBmRZ89d+59V1ak6NfZ7sy5UW6O9+ZHWqzIMGVlZSr39Ns0rHNg93av3nFHN4kr5UoOqaNilqwMNqpyWq+xmv/KONSivql7OYQzChOQzM7KU6kzRvNo3ZMiKJPnzpYpjO+TseiNTmzef/YdxbcwkWPPz87V27VpJ0ne+8x0999xzCgQCOnz4sP71X/9VbW1tSktL0/reg8hIOnPmjG644QbdcMMNevbZZ4e8vgsvvFCFhYVqb2/XV7/6Vb3++usKBAJ66aWX9O1vf1uSNHfu3AHdFbz5zW9Wenq6amtrde+996qqqkqdnZ3atGmT/vM//1OSdNFFFyknJ2fAOjE6HDm5Sg/WyIiMB6lZdZGEqWVGkj/LT9lyOpxdnxkyZejAiToduu3L6nCd/bXPkKGFU6MPqIaxy5GbL/nbNKWtPfIrsGXLqK9TTUvshJ7V0aZqj0t/LZ6lxTXqmk/Kb21RR3qdnM3tSvMf6vnctKUTdoqO5a5RhzNSaQobkr+zXVv2VPVpLWv5OhRMTdEZ75yzMXa1hvV1zdtbYVaaUrxeucJnW7B2draq2Rccc4PjhZsa9HphhaTI+0/d25IS8utN2zZpXmmkb+MnfuTTvJy5mps1W9/5WYM23rhl2IOX9ffZ25fqs7cvpaIBAACAuAzDUEqbX2kN7XJ1BvskrArKF+rH8wL67sr9mpVSJdMORF6dt0I67XpRO/dvUvi5rVIopOXVkqNXdbyk5bTWr54f+WPzZv3PE6nyVJ/S6dbf6i8FT8jTuVV+Z1BtbqnJbevZfX/U3prWnvlzvpGj9z18hXZMKYp0f+VwyLh4nRZOy5LbGT0V8+Obn9X8l45pwcsnZG7ecu52GiRJWalZSgsZSgsNs+/Vxsaz/+IwM7IUMmxVNjTpi1s26WPbd2mOe7/e7c3XssqLtayS7gEmkjHVHvmjH/2ojh8/rqqqKn33u9/Vd7/73Z5phmHoE5/4hDIy+v7SY9u2gsFIyzVrCK8Id3M6nbr55pv1ta99TadOndLdd9/dZ3p6erpuuummAfNlZmbqX/7lX/Tggw/qwIED+vznP99n+pQpU/ThD394yHFg+MzMbKX6T8j2zI10FNBVFnaP7lja2q67175dX3j5+Z7+aJp//t96dvrSntarMgwV3/hB5XtTzm/wGDEz3as6V0gLg3UKG5HBowr9Tp1obNfCaVkDvm/btuq3/EVfm1ms2Q1mz+BWzkBQbzLbdO0/bNKOb+TIHz6oFHO+mlO7kolWpJIldY3cKcmywmo5U6+9VWe0qCRHkiH/G6/phfJlCjgz1O6OjAjp6WqMapkDb9TF+RkyPGl9uggwbUPBYEjtgbC8KVGK5fXrZe/cKUkyli49t0nHXj9idX7tbu3LnylDkV/7G9xhbZy7X7PKLP3Tr/b1mc0YxQrgSJOzAAAAmGTidCs1rWyRmiTd8d7H9Y+fm6NLD2UpNehTZiCg760ulFTS893KhkhXYnNrD+rS3c8rpXfDqaVLtUzSMwclbd6s4GXr9On5h1WfHmloEW5rUVV9q1a62xSqOanLq3N1afVqBR2mZIclh0Mur1fzixhceaxYWrRUDvOv52z5hmFod6Zf9WmS1KK59W1a8Y4fy3jxX8/ZOpE85y3B+qtf/WrQ73i9Xt177736+c9/ro0bNyoQiDSvnzp1qj7ykY9E7R6goKBgSMuOpqKiQvfff79+/OMfa8eOHT2txxYsWKBPfOITmjZtWtT5LrjgAt1777368Y9/rNdff11S5MJZuXKlPvaxjykra2CSB6PHzMhSqtWm1ECtpAJ5/ZEc66FcqcUM6boHv6sp+19SSqhJfme2pMhgV22usz9HZqRlaVFl9OOLse+U3aYpbbXyucoiH4TDOnGsRsG5RXI5zv4abLW3qW7j0/rD715QoKt/pZApGaGwVnzjKyqaFXnF5rO3L9Xuw8/rwV3V2jxzelfbaFvq6jze6vUDc6i2Wlv+WK09jpD+euJlBUyPKqaWa3a9rf15UoszrPaSdM06vF01noUDYi+dki0z1S2PYcmwQ7INpxacCsn+2WNq/MYeef/3/w2YJ7TvVflyUmU5TbnNkFJte9RHuOxmy5b/8BsKpqVo10M/UGdqgZZ0DQjgfP8Hdc3ly/hhAgAAAGPLX6Mnyfr/cP/41Dp9dGODglZYyshXZudJmdbZBKtt2yo9flAX7t6k1P4vyPVr5OC0TX3sleN6+MJIgrXTYajhh/+pf/P/TnVpM2V63y7LkJyWJNlSKKRZpQVKc4+pdm44x7Zl+1R4MiwrLUWpHSGlrljLW3oT1Ji7slNTU3XjjTfq2muv1YkTJ+TxeFRSUjL4jAkqKirS7bffrqamJtXW1iovL095eQM7nO5v5syZ+upXv6q6ujo1NDSosLCQxOp5YnozJZdL//TiTm0vvUySU0tqJCNsab7T1IwpGfLXF8gbOCG/M1sLq8Nqc9k9rQolaUqnT+V56UnbBoyMLy1Ffyw+IWmFwl2Jxl8//yNdcsF9mtPVn5EdDun+j1WoIXOlGr3lkqROpyTb1gx3hlbNLOhZ3uaPbJZt2zrwy5/r6d0vSZJMK6TuwdSWnDa0Z4qtsCGFntuqF0oi3VHUF0iSX2FDOppryCGpsyhPP/rwF3XfF+dpVf1MdTo9aqrZqqrybK0svkAFOZHz7ouX3qmbtm2U35UrSQoblk54vCoJBmW4XD2xWb4OdeR79V/z/bIMUz53rT5fdUju8rPdl1gd7Qrsf012OCz3zNly5AxehsXiP7Jf312TrhPZy9SUlqvltWenlU3JIrkKAACAcav9ywHt+EWkVWpJfoUss0rZnU1qSMuWw5Iy2ut1ya6tMoMhDZYuMTZv0dM3TpfP4VPY4ZFhhWW1t2rbtLWyDUef7gYkybBsLZpz7nIbSFD3QGeVS8/J4i1DcjW2So2tSgtIpifOuB0Y18ZcgrWbx+PRrFmzztv6srOzlZ2dPez58vPzlZ+fP/oBISbD4ZCvrEQZLx3SZQf/pj1TF6rD5VFZTY0u2vp7SZIjb4q8gZOq9yyQafW9sxn+oFa3VMs8Ry0AcR4sW6q/73pZHztUo79UTJVsKT1o6/Xqpp4Eq/+K9dLUKcoOlKsxLfK6jyckZbX59ebAMTkdffs9MgxDJcVTlPnSEQXs0p5Wqw5LSvd3KN3fIMtR3NMVRWZnT+8UkqQlZyIdUnzx5k9rWk666tNt5fle09GcFZEvtDTpVeMJmcZ7IutLS1eG3aKwWaBDOSE56xu1zWmq4qq3acpTf5LR1Y9w8Mh++WxDx7NXqDl1mkJOl379wiG9Oa9EBd4UBY4d0a7PfkH/XZyqsOGSO9PUv3zkbpVke+TMzZdhDr2r7XBzk+ozvNpRfIlCDo88IanVEZJhS17LqXnlhcM8UAAAAMB5cPHQ+7Jcti/Sb2bnrpfl/78btPjUFp3KLNa0QIpmHzmodH9YknNIy6ybW6YNfz2hJ+fNkmQrZIVkGw4tOS0dze4aWNeKPDfMcYY1xZua0OZhfOvOSETGiMFENWYTrEA8r09xKOjv1MYF9XrnG1tlGYYWZM6WXJEklyMnXx98uVU7io/oZMZ02ZI6nJFWjVltJ1X+x98ndwMwIps/sll2MKA/ve1iSVMlRfoxPVVTr2ZfkTJTHDqd6tHh/CVaUCstrYnMl+Lr1Lt//j3lpEdvhenIK5C39WVZDktSWddybS04+LyOpXfIl5ojKa1nsKf0gFTeFEm22gppeuMpVUyPdDvgKJuhnOMHpQZL7SkFSgs0qKnAcXZd2blK7TUC6c4iSfLqb9Pn6YObXtGlK+cqMztTwZpq/bVsuY5nF0bWa9ja+Ppz6pxSrLd/9v16qWyxfj+zSKHuO3W79Ptv/UDpoU7NbTmlRT94VJlZQxjMbf16+RyW/lKxRiGHZ8Dk7M5WlRUxeB8AAADGoAReuXbPWagPbQ+p2OdQcetxeQOmPG2+YS3j3z70uF783eVaVBtpHOa0pGz/2emRxhm23vnKNs3f/eKwY8S599nbl0piHAiMHAlWjEsbb9yilpsMVVa5ZUvKaurs6S9TkgynU+FwUEtP7NK2lV7VenNlSGoqTtOT//TkOeu/EueP4XKrpPW0vIGAwqZbnakOWa3NOnnBGrk6q/Xcksu0pEYKm5F/RjCk1d/9RszkqiQ5S8pkmobKGrbrzYePqjUlXeU11apxt+hAiqX/u2WzXi+YpbrUFE1p8GlDXUBnMvMkZSqvo1GXHdzVc2499i9b9ZntUzTTd0I5vhMKGba+/OnXe9ZlZuUoNVQnw7b6vD5kS/rtUz/VE38ISh0v6QuX3qyTmYV9Wsu2B9rU/utf6K71l6uy4ewvoj6ndOEJaWdBkyTpucw8rfrDC1px8QqtnJ4T97wPy9IrecVqNiItgD3dPSTYUnqgXW957Xk5ogzaBQAAAIxHZmqqwqfPKKXZlGzJEzBkBINnB30dQtLWWThVXl+z8trP6MXSKVpaIy2sjdT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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-02-11   9.50   9.94   9.50   9.50\n",
      "1    2010-02-12   9.10   9.34   9.03   9.17\n",
      "2    2010-02-22   9.09   9.30   8.82   9.22\n",
      "3    2010-02-23   9.10   9.22   8.88   9.10\n",
      "4    2010-02-24   9.10   9.45   9.02   9.40\n",
      "...         ...    ...    ...    ...    ...\n",
      "3384 2024-12-19  18.75  20.63  18.74  20.63\n",
      "3385 2024-12-20  19.62  20.59  19.28  20.06\n",
      "3386 2024-12-23  18.11  19.41  18.05  18.05\n",
      "3387 2024-12-24  16.25  16.58  16.25  16.25\n",
      "3388 2024-12-25  14.63  14.63  14.63  14.63\n",
      "\n",
      "[3389 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3389 entries, 0 to 3388\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3389 non-null   datetime64[ns]\n",
      " 1   open        3389 non-null   float64       \n",
      " 2   high        3389 non-null   float64       \n",
      " 3   low         3389 non-null   float64       \n",
      " 4   close       3389 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 132.5 KB\n",
      "None\n"
     ]
    },
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-02-11</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-02-22</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.30</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-02-23</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.22</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-02-24</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.40</td>\n",
       "      <td>9.278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>18.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.74</td>\n",
       "      <td>20.63</td>\n",
       "      <td>19.708</td>\n",
       "      <td>19.168</td>\n",
       "      <td>18.606000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>19.62</td>\n",
       "      <td>20.59</td>\n",
       "      <td>19.28</td>\n",
       "      <td>20.06</td>\n",
       "      <td>19.710</td>\n",
       "      <td>19.325</td>\n",
       "      <td>18.630667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>18.11</td>\n",
       "      <td>19.41</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.05</td>\n",
       "      <td>19.208</td>\n",
       "      <td>19.307</td>\n",
       "      <td>18.552333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3387</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.25</td>\n",
       "      <td>18.748</td>\n",
       "      <td>19.114</td>\n",
       "      <td>18.438000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>17.924</td>\n",
       "      <td>18.756</td>\n",
       "      <td>18.273333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3389 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10         30\n",
       "0    2010-02-11   9.50   9.94   9.50   9.50     NaN     NaN        NaN\n",
       "1    2010-02-12   9.10   9.34   9.03   9.17     NaN     NaN        NaN\n",
       "2    2010-02-22   9.09   9.30   8.82   9.22     NaN     NaN        NaN\n",
       "3    2010-02-23   9.10   9.22   8.88   9.10     NaN     NaN        NaN\n",
       "4    2010-02-24   9.10   9.45   9.02   9.40   9.278     NaN        NaN\n",
       "...         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "3384 2024-12-19  18.75  20.63  18.74  20.63  19.708  19.168  18.606000\n",
       "3385 2024-12-20  19.62  20.59  19.28  20.06  19.710  19.325  18.630667\n",
       "3386 2024-12-23  18.11  19.41  18.05  18.05  19.208  19.307  18.552333\n",
       "3387 2024-12-24  16.25  16.58  16.25  16.25  18.748  19.114  18.438000\n",
       "3388 2024-12-25  14.63  14.63  14.63  14.63  17.924  18.756  18.273333\n",
       "\n",
       "[3389 rows x 8 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0          14.63  14.63\n",
       "1          16.25  16.58\n",
       "2          18.11  19.41\n",
       "3          19.62  20.59\n",
       "4          18.75  20.63\n",
       "...          ...    ...\n",
       "3384        9.10   9.45\n",
       "3385        9.10   9.22\n",
       "3386        9.09   9.30\n",
       "3387        9.10   9.34\n",
       "3388        9.50   9.94\n",
       "\n",
       "[3389 rows x 2 columns]>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.5 ],\n",
       "       [ 9.17],\n",
       "       [ 9.22],\n",
       "       ...,\n",
       "       [18.05],\n",
       "       [16.25],\n",
       "       [14.63]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14651.0</th>\n",
       "      <td>9.50</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14652.0</th>\n",
       "      <td>9.10</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14662.0</th>\n",
       "      <td>9.09</td>\n",
       "      <td>9.30</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14663.0</th>\n",
       "      <td>9.10</td>\n",
       "      <td>9.22</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14664.0</th>\n",
       "      <td>9.10</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>18.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.74</td>\n",
       "      <td>20.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>19.62</td>\n",
       "      <td>20.59</td>\n",
       "      <td>19.28</td>\n",
       "      <td>20.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>18.11</td>\n",
       "      <td>19.41</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>16.25</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3389 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14651.0   9.50   9.94   9.50   9.50\n",
       "14652.0   9.10   9.34   9.03   9.17\n",
       "14662.0   9.09   9.30   8.82   9.22\n",
       "14663.0   9.10   9.22   8.88   9.10\n",
       "14664.0   9.10   9.45   9.02   9.40\n",
       "...        ...    ...    ...    ...\n",
       "20076.0  18.75  20.63  18.74  20.63\n",
       "20077.0  19.62  20.59  19.28  20.06\n",
       "20080.0  18.11  19.41  18.05  18.05\n",
       "20081.0  16.25  16.58  16.25  16.25\n",
       "20082.0  14.63  14.63  14.63  14.63\n",
       "\n",
       "[3389 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14651.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.94</td>\n",
       "      <td>9.50</td>\n",
       "      <td>9.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14652.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.34</td>\n",
       "      <td>9.03</td>\n",
       "      <td>9.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14662.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>9.30</td>\n",
       "      <td>8.82</td>\n",
       "      <td>9.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14663.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.22</td>\n",
       "      <td>8.88</td>\n",
       "      <td>9.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14664.0</td>\n",
       "      <td>9.10</td>\n",
       "      <td>9.45</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>18.75</td>\n",
       "      <td>20.63</td>\n",
       "      <td>18.74</td>\n",
       "      <td>20.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>19.62</td>\n",
       "      <td>20.59</td>\n",
       "      <td>19.28</td>\n",
       "      <td>20.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>18.11</td>\n",
       "      <td>19.41</td>\n",
       "      <td>18.05</td>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3387</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.58</td>\n",
       "      <td>16.25</td>\n",
       "      <td>16.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3388</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "      <td>14.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3389 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14651.0   9.50   9.94   9.50   9.50\n",
       "1     14652.0   9.10   9.34   9.03   9.17\n",
       "2     14662.0   9.09   9.30   8.82   9.22\n",
       "3     14663.0   9.10   9.22   8.88   9.10\n",
       "4     14664.0   9.10   9.45   9.02   9.40\n",
       "...       ...    ...    ...    ...    ...\n",
       "3384  20076.0  18.75  20.63  18.74  20.63\n",
       "3385  20077.0  19.62  20.59  19.28  20.06\n",
       "3386  20080.0  18.11  19.41  18.05  18.05\n",
       "3387  20081.0  16.25  16.58  16.25  16.25\n",
       "3388  20082.0  14.63  14.63  14.63  14.63\n",
       "\n",
       "[3389 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2611b99b1a0>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\w\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.0929 - val_loss: 0.0046\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0146 - val_loss: 0.0024\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0023 - val_loss: 0.0014\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0012 - val_loss: 0.0014\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0010 - val_loss: 0.0014\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0010 - val_loss: 0.0014\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.8773e-04 - val_loss: 0.0014\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.6677e-04 - val_loss: 0.0014\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.5135e-04 - val_loss: 0.0014\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.3444e-04 - val_loss: 0.0014\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.1943e-04 - val_loss: 0.0014\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 9.0544e-04 - val_loss: 0.0013\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.9229e-04 - val_loss: 0.0013\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.7911e-04 - val_loss: 0.0013\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.6587e-04 - val_loss: 0.0013\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.5248e-04 - val_loss: 0.0013\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.3795e-04 - val_loss: 0.0013\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.2177e-04 - val_loss: 0.0013\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 8.0437e-04 - val_loss: 0.0013\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.8588e-04 - val_loss: 0.0013\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.6713e-04 - val_loss: 0.0012\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.4758e-04 - val_loss: 0.0012\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.3377e-04 - val_loss: 0.0012\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.2036e-04 - val_loss: 0.0012\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.0933e-04 - val_loss: 0.0012\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.0100e-04 - val_loss: 0.0012\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.9453e-04 - val_loss: 0.0012\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.8414e-04 - val_loss: 0.0012\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.6418e-04 - val_loss: 0.0012\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 6.5776e-04 - val_loss: 0.0012\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 2.25617\n",
      "均方根误差: 1.50206\n",
      "平均绝对误差: 1.10066\n",
      "R2: 0.28585\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
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